Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

Papers

Deconvolutional Networks

• paper: http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf
• video: https://ipam.wistia.com/medias/zd0qnekkwc
• presentation: https://mathinstitutes.org/videos/videos/3295

Visualizing and Understanding Convolutional Network

• intro: ECCV 2014
• arxiv: http://arxiv.org/abs/1311.2901
• slides: https://courses.cs.washington.edu/courses/cse590v/14au/cse590v_dec5_DeepVis.pdf
• slides: http://videolectures.net/site/normal_dl/tag=921098/eccv2014_zeiler_convolutional_networks_01.pdf
• video: http://videolectures.net/eccv2014_zeiler_convolutional_networks/
• chs: http://blog.csdn.net/kklots/article/details/17136059
• github: https://github.com/piergiaj/caffe-deconvnet

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

• intro: ICLR 2014 workshop
• arxiv: http://arxiv.org/abs/1312.6034
• github: https://github.com/yasunorikudo/vis-cnn

Understanding Deep Image Representations by Inverting Them

• arxiv: http://arxiv.org/abs/1412.0035
• github: https://github.com/aravindhm/deep-goggle

deepViz: Visualizing Convolutional Neural Networks for Image Classification

• paper: http://vis.berkeley.edu/courses/cs294-10-fa13/wiki/images/f/fd/DeepVizPaper.pdf
• github: https://github.com/bruckner/deepViz

Inverting Convolutional Networks with Convolutional Networks

• arxiv: http://arxiv.org/abs/1506.02753

Understanding Neural Networks Through Deep Visualization

• project page: http://yosinski.com/deepvis
• arxiv: http://arxiv.org/abs/1506.06579
• github: https://github.com/yosinski/deep-visualization-toolbox

Visualizing Higher-Layer Features of a Deep Network

• paper: http://www.iro.umontreal.ca/~lisa/publications2/index.php/publications/show/247

Generative Modeling of Convolutional Neural Networks

• project page: http://www.stat.ucla.edu/~yang.lu/Project/generativeCNN/main.html
• arxiv: http://arxiv.org/abs/1412.6296
• code: http://www.stat.ucla.edu/~yang.lu/Project/generativeCNN/doc/caffe-generative.zip

Understanding Intra-Class Knowledge Inside CNN

• arxiv: http://arxiv.org/abs/1507.02379

Learning FRAME Models Using CNN Filters for Knowledge Visualization

• project page: http://www.stat.ucla.edu/~yang.lu/project/deepFrame/main.html
• arxiv: http://arxiv.org/abs/1509.08379
• code: http://www.stat.ucla.edu/~yang.lu/project/deepFrame/doc/code.zip

Convergent Learning: Do different neural networks learn the same representations?

• intro: ICLR 2016
• arxiv: http://arxiv.org/abs/1511.07543
• github: https://github.com/yixuanli/convergent_learning
• video: http://videolectures.net/iclr2016_yosinski_convergent_learning/

Visualizing and Understanding Deep Texture Representations

• homepage: http://vis-www.cs.umass.edu/texture/
• arxiv: http://arxiv.org/abs/1511.05197
• paper: https://people.cs.umass.edu/~smaji/papers/texture-cvpr16.pdf

Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images

• arxiv: http://arxiv.org/abs/1512.02017

An Interactive Node-Link Visualization of Convolutional Neural Networks

• homepage: http://scs.ryerson.ca/~aharley/vis/
• code: http://scs.ryerson.ca/~aharley/vis/source.zip
• demo: http://scs.ryerson.ca/~aharley/vis/conv/
• review: http://www.popsci.com/gaze-inside-mind-artificial-intelligence

Learning Deep Features for Discriminative Localization

• project page: http://cnnlocalization.csail.mit.edu/
• arxiv: http://arxiv.org/abs/1512.04150
• github: https://github.com/metalbubble/CAM
• blog: http://jacobcv.blogspot.com/2016/08/class-activation-maps-in-keras.html
• github: https://github.com/jacobgil/keras-cam

Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks

• intro: Visualization for Deep Learning workshop. ICML 2016
• arxiv: http://arxiv.org/abs/1602.03616
• homepage: http://www.evolvingai.org/nguyen-yosinski-clune-2016-multifaceted-feature
• github: https://github.com/Evolving-AI-Lab/mfv

A New Method to Visualize Deep Neural Networks

• arxiv: http://arxiv.org/abs/1603.02518

A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks

• arxiv: http://arxiv.org/abs/1606.07757

VisualBackProp: visualizing CNNs for autonomous driving

VisualBackProp: efficient visualization of CNNs

• arxiv: https://arxiv.org/abs/1611.05418
• github: https://github.com/mbojarski/VisualBackProp

Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

• arxiv: https://arxiv.org/abs/1610.02391
• github: https://github.com/ramprs/grad-cam/
• github(Keras): https://github.com/jacobgil/keras-grad-cam
• github(TensorFlow): https://github.com/Ankush96/grad-cam.tensorflow

Grad-CAM: Why did you say that?

• intro: NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems
• intro: extended abstract version of arXiv:1610.02391
• arxiv: https://arxiv.org/abs/1611.07450

Visualizing Residual Networks

• intro: UC Berkeley CS 280 final project report
• arxiv: https://arxiv.org/abs/1701.02362

Visualizing Deep Neural Network Decisions: Prediction Difference Analysis

• intro: University of Amsterdam & Canadian Institute of Advanced Research & Vrije Universiteit Brussel
• intro: ICLR 2017
• arxiv: https://arxiv.org/abs/1702.04595
• github: https://github.com/lmzintgraf/DeepVis-PredDiff

ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models

• intro: Georgia Tech & Facebook
• arxiv: https://arxiv.org/abs/1704.01942

Picasso: A Neural Network Visualizer

• arxiv: https://arxiv.org/abs/1705.05627
• github: https://github.com/merantix/picasso
• blog: https://medium.com/merantix/picasso-a-free-open-source-visualizer-for-cnns-d8ed3a35cfc5

CNN Fixations: An unraveling approach to visualize the discriminative image regions

• arxiv: https://arxiv.org/abs/1708.06670
• github: https://github.com/utsavgarg/cnn-fixations

A Forward-Backward Approach for Visualizing Information Flow in Deep Networks

• intro: NIPS 2017 Symposium on Interpretable Machine Learning. Iowa State University
• arxiv: https://arxiv.org/abs/1711.06221

Using KL-divergence to focus Deep Visual Explanation

https://arxiv.org/abs/1711.06431

An Introduction to Deep Visual Explanation

• intro: NIPS 2017 - Workshop Interpreting, Explaining and Visualizing Deep Learning
• arxiv: https://arxiv.org/abs/1711.09482

Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks

https://arxiv.org/abs/1712.06302

Visualizing the Loss Landscape of Neural Nets

• intro: University of Maryland & United States Naval Academy
• arxiv: https://arxiv.org/abs/1712.09913

Visualizing Deep Similarity Networks

https://arxiv.org/abs/1901.00536

Interpreting Convolutional Neural Networks

Network Dissection: Quantifying Interpretability of Deep Visual Representations

• intro: CVPR 2017 oral. MIT
• project page: http://netdissect.csail.mit.edu/
• arxiv: https://arxiv.org/abs/1704.05796
• github: https://github.com/CSAILVision/NetDissect

Interpreting Deep Visual Representations via Network Dissection

https://arxiv.org/abs/1711.05611

Methods for Interpreting and Understanding Deep Neural Networks

• intro: Technische Universit¨at Berlin & Fraunhofer Heinrich Hertz Institute
• arxiv: https://arxiv.org/abs/1706.07979

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability

• intro: NIPS 2017. Google Brain & Uber AI Labs
• arxiv: https://arxiv.org/abs/1706.05806
• github: https://github.com/google/svcca/
• blog: https://research.googleblog.com/2017/11/interpreting-deep-neural-networks-with.html

Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples

• intro: Tsinghua University
• arxiv: https://arxiv.org/abs/1708.05493

Interpretable Convolutional Neural Networks

https://arxiv.org/abs/1710.00935

Interpreting Convolutional Neural Networks Through Compression

• intro: NIPS 2017 Symposium on Interpretable Machine Learning
• arxiv: https://arxiv.org/abs/1711.02329

Interpreting Deep Neural Networks

• blog: http://www.shallowmind.co/jekyll/pixyll/2017/12/30/tree-regularization/

Interpreting CNNs via Decision Trees

https://arxiv.org/abs/1802.00121

Visual Interpretability for Deep Learning: a Survey

https://arxiv.org/abs/1802.00614

Interpreting Deep Classifier by Visual Distillation of Dark Knowledge

• intro: University of Edinburgh & Huawei Research America
• arxiv: https://arxiv.org/abs/1803.04042

How convolutional neural network see the world - A survey of convolutional neural network visualization methods

• intro: Mathematical Foundations of Computing. George Mason University & Clarkson University
• arxiv: https://arxiv.org/abs/1804.11191

Understanding Regularization to Visualize Convolutional Neural Networks

• intro: Konica Minolta Laboratory Europe & Technical University of Munich
• arxiv: https://arxiv.org/abs/1805.00071

Deeper Interpretability of Deep Networks

• intro: University of Glasgow & University of Oxford & University of California
• arxiv: https://arxiv.org/abs/1811.07807

Interpretable CNNs

https://arxiv.org/abs/1901.02413

Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks

https://arxiv.org/abs/1901.02184

Interpretable BoW Networks for Adversarial Example Detection

https://arxiv.org/abs/1901.02229

Deep Features Analysis with Attention Networks

• intro: In AAAI-19 Workshop on Network Interpretability for Deep Learning
• arxiv: https://arxiv.org/abs/1901.10042

Understanding Neural Networks via Feature Visualization: A survey

• intro: A book chapter in an Interpretable ML book (http://www.interpretable-ml.org/book/)
• arxiv: https://arxiv.org/abs/1904.08939

Explaining Neural Networks via Perturbing Important Learned Features

https://arxiv.org/abs/1911.11081

Interpreting Adversarially Trained Convolutional Neural Networks

• intro: ICML 2019
• arxiv: https://arxiv.org/abs/1905.09797
• github: https://github.com/PKUAI26/AT-CNN

Projects

Interactive Deep Neural Net Hallucinations

• project page: http://317070.github.io/Dream/
• github: https://github.com/317070/Twitch-plays-LSD-neural-net

torch-visbox

• github: https://github.com/Aysegul/torch-visbox

draw_convnet: Python script for illustrating Convolutional Neural Network (ConvNet)

• github: https://github.com/gwding/draw_convnet

Caffe prototxt visualization

• intro: Recommended by Kaiming He
• github: https://github.com/ethereon/netscope
• quickstart: http://ethereon.github.io/netscope/quickstart.html
• demo: http://ethereon.github.io/netscope/#/editor

Keras Visualization Toolkit

• github: https://github.com/raghakot/keras-vis
• docs: https://raghakot.github.io/keras-vis/

mNeuron: A Matlab Plugin to Visualize Neurons from Deep Models

• project page: http://vision03.csail.mit.edu/cnn_art/
• github: https://github.com/donglaiw/mNeuron

cnnvis-pytorch

• intro: visualization of CNN in PyTorch
• github: https://github.com/leelabcnbc/cnnvis-pytorch

VisualDL

• intro: A platform to visualize the deep learning process
• homepage: http://visualdl.paddlepaddle.org/
• github: https://github.com/PaddlePaddle/VisualDL

Blogs

“Visualizing GoogLeNet Classes”

http://auduno.com/post/125362849838/visualizing-googlenet-classes

Visualizing CNN architectures side by side with mxnet

• blog: http://josephpcohen.com/w/visualizing-cnn-architectures-side-by-side-with-mxnet/

How convolutional neural networks see the world: An exploration of convnet filters with Keras

• blog: http://blog.keras.io/how-convolutional-neural-networks-see-the-world.html
• github: https://github.com/fchollet/keras/blob/master/examples/conv_filter_visualization.py

Visualizing Deep Learning with t-SNE (Tutorial and Video)

• blog: https://medium.com/@awjuliani/visualizing-deep-learning-with-t-sne-tutorial-and-video-e7c59ee4080c#.ubhijafw7
• github: https://github.com/awjuliani/3D-TSNE

Peeking inside Convnets

• blog: https://auduno.github.io/2016/06/18/peeking-inside-convnets/

Visualizing Features from a Convolutional Neural Network

• blog: http://kvfrans.com/visualizing-features-from-a-convolutional-neural-network/
• github: https://github.com/kvfrans/feature-visualization

Visualizing Deep Neural Networks Classes and Features

http://ankivil.com/visualizing-deep-neural-networks-classes-and-features/

Visualizing parts of Convolutional Neural Networks using Keras and Cats

• blog: https://hackernoon.com/visualizing-parts-of-convolutional-neural-networks-using-keras-and-cats-5cc01b214e59#.bt6bb13dk
• github: https://github.com/erikreppel/visualizing_cnns

Visualizing convolutional neural networks

• intro: How to build convolutional neural networks from scratch w/ Tensorflow
• blog: https://www.oreilly.com/ideas/visualizing-convolutional-neural-networks
• github: https://github.com//wagonhelm/Visualizing-Convnets/

Tools

Topological Visualisation of a Convolutional Neural Network

http://terencebroad.com/convnetvis/vis.html

Visualization of Places-CNN and ImageNet CNN

• homepage: http://places.csail.mit.edu/visualizationCNN.html
• DrawCNN: http://people.csail.mit.edu/torralba/research/drawCNN/drawNet.html

Visualization of a feed forward Neural Network using MNIST dataset

• homepage: http://nn-mnist.sennabaum.com/
• github: https://github.com/csenn/nn-visualizer

CNNVis: Towards Better Analysis of Deep Convolutional Neural Networks.

http://shixialiu.com/publications/cnnvis/demo/

Quiver: Interactive convnet features visualization for Keras

• homepage: https://jakebian.github.io/quiver/
• github: https://github.com/jakebian/quiver

Netron

• intro: Visualizer for deep learning and machine learning models
• github: https://github.com/lutzroeder/netron

Papers

Restricted Boltzmann Machine (RBM)

Papers

Tutorials

Learning A Deep Compact Image Representation for Visual Tracking

• intro: NIPS 2013
• intro: DLT
• project page: http://winsty.net/dlt.html

Hierarchical Convolutional Features for Visual Tracking

• intro: ICCV 2015
• project page: https://sites.google.com/site/jbhuang0604/publications/cf2
• github: https://github.com/jbhuang0604/CF2

Robust Visual Tracking via Convolutional Networks

• arxiv: http://arxiv.org/abs/1501.04505
• paper: http://kaihuazhang.net/CNT.pdf
• code: http://kaihuazhang.net/CNT_matlab.rar

Transferring Rich Feature Hierarchies for Robust Visual Tracking

• intro: SO-DLT
• arxiv: http://arxiv.org/abs/1501.04587
• slides: http://valse.mmcheng.net/ftp/20150325/RVT.pptx

Learning Multi-Domain Convolutional Neural Networks for Visual Tracking

• intro: The Winner of The VOT2015 Challenge
• keywords: Multi-Domain Network (MDNet)
• homepage: http://cvlab.postech.ac.kr/research/mdnet/
• arxiv: http://arxiv.org/abs/1510.07945
• github: https://github.com/HyeonseobNam/MDNet

RATM: Recurrent Attentive Tracking Model

• arxiv: http://arxiv.org/abs/1510.08660
• github: https://github.com/saebrahimi/RATM

Understanding and Diagnosing Visual Tracking Systems

• intro: ICCV 2015
• project page: http://winsty.net/tracker_diagnose.html
• paper: http://winsty.net/papers/diagnose.pdf
• code(Matlab): http://120.52.72.43/winsty.net/c3pr90ntcsf0/diagnose/diagnose_code.zip

Recurrently Target-Attending Tracking

• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Cui_Recurrently_Target-Attending_Tracking_CVPR_2016_paper.html
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Cui_Recurrently_Target-Attending_Tracking_CVPR_2016_paper.pdf

Visual Tracking with Fully Convolutional Networks

• intro: ICCV 2015
• paper: http://202.118.75.4/lu/Paper/ICCV2015/iccv15_lijun.pdf
• github: https://github.com/scott89/FCNT

Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks

• intro: AAAI 2016
• arxiv: http://arxiv.org/abs/1602.00991
• github: https://github.com/pondruska/DeepTracking

Learning to Track at 100 FPS with Deep Regression Networks

• intro: ECCV 2015
• intro: GOTURN: Generic Object Tracking Using Regression Networks
• project page: http://davheld.github.io/GOTURN/GOTURN.html
• arxiv: http://arxiv.org/abs/1604.01802
• github: https://github.com/davheld/GOTURN

Learning by tracking: Siamese CNN for robust target association

• arxiv: http://arxiv.org/abs/1604.07866

Fully-Convolutional Siamese Networks for Object Tracking

• intro: ECCV 2016
• intro: State-of-the-art performance in arbitrary object tracking at 50-100 FPS with Fully Convolutional Siamese networks
• project page: http://www.robots.ox.ac.uk/~luca/siamese-fc.html
• arxiv: http://arxiv.org/abs/1606.09549
• github(official): https://github.com/bertinetto/siamese-fc
• github(official): https://github.com/torrvision/siamfc-tf
• valse-video: http://www.iqiyi.com/w_19ruirwrel.html#vfrm=8-8-0-1

Hedged Deep Tracking

• project page(paper+code): https://sites.google.com/site/yuankiqi/hdt
• paper: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnx5dWFua2lxaXxneDoxZjc2MmYwZGIzNjFhYTRl

Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking

• intro: ROLO is short for Recurrent YOLO, aimed at simultaneous object detection and tracking
• project page: http://guanghan.info/projects/ROLO/
• arxiv: http://arxiv.org/abs/1607.05781
• github: https://github.com/Guanghan/ROLO

Visual Tracking via Shallow and Deep Collaborative Model

• arxiv: http://arxiv.org/abs/1607.08040

Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

• intro: ECCV 2016
• intro: OTB-2015 (+5.1% in mean OP), Temple-Color (+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate)
• keywords: Continuous Convolution Operator Tracker (C-COT)
• project page: http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html
• arxiv: http://arxiv.org/abs/1608.03773
• github(MATLAB): https://github.com/martin-danelljan/Continuous-ConvOp

Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network

• keywords: Predictive Vision Model (PVM)
• arxiv: http://arxiv.org/abs/1607.06854
• github: https://github.com/braincorp/PVM

Modeling and Propagating CNNs in a Tree Structure for Visual Tracking

• arxiv: http://arxiv.org/abs/1608.07242

Robust Scale Adaptive Kernel Correlation Filter Tracker With Hierarchical Convolutional Features

• paper: http://ieeexplore.ieee.org/document/7496863/

Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks

• arxiv: https://arxiv.org/abs/1609.09365

OTB Results: visual tracker benchmark results

• github: https://github.com/foolwood/benchmark_results

Convolutional Regression for Visual Tracking

• arxiv: https://arxiv.org/abs/1611.04215

Semantic tracking: Single-target tracking with inter-supervised convolutional networks

• arxiv: https://arxiv.org/abs/1611.06395

SANet: Structure-Aware Network for Visual Tracking

• arxiv: https://arxiv.org/abs/1611.06878

ECO: Efficient Convolution Operators for Tracking

• intro: CVPR 2017
• project page: http://www.cvl.isy.liu.se/research/objrec/visualtracking/ecotrack/index.html
• arxiv: https://arxiv.org/abs/1611.09224
• github: https://github.com/martin-danelljan/ECO

Dual Deep Network for Visual Tracking

• arxiv: https://arxiv.org/abs/1612.06053

Deep Motion Features for Visual Tracking

• intro: ICPR 2016. Best paper award in the “Computer Vision and Robot Vision” track
• arxiv: https://arxiv.org/abs/1612.06615

Globally Optimal Object Tracking with Fully Convolutional Networks

• arxiv: https://arxiv.org/abs/1612.08274

Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation

• arxiv: https://arxiv.org/abs/1701.00561
• bitbucket: https://bitbucket.org/xinke_wang/msdat

Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies

• arxiv: https://arxiv.org/abs/1701.01909

Large Margin Object Tracking with Circulant Feature Maps

• intro: CVPR 2017
• intro: The experimental results demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms on the challenging benchmark sequences while runs at speed in excess of 80 frames per secon
• arxiv: https://arxiv.org/abs/1703.05020
• notes: https://zhuanlan.zhihu.com/p/25761718

DCFNet: Discriminant Correlation Filters Network for Visual Tracking

• arxiv: https://arxiv.org/abs/1704.04057
• github: https://github.com/foolwood/DCFNet

End-to-end representation learning for Correlation Filter based tracking

• intro: CVPR 2017. University of Oxford
• intro: Training a Correlation Filter end-to-end allows lightweight networks of 2 layers (600 kB) to achieve state-of-the-art performance in tracking, at high-speed.
• project page: http://www.robots.ox.ac.uk/~luca/cfnet.html
• arxiv: https://arxiv.org/abs/1704.06036
• gtihub: https://github.com/bertinetto/cfnet

Context-Aware Correlation Filter Tracking

• intro: CVPR 2017 Oral
• project page: https://ivul.kaust.edu.sa/Pages/pub-ca-cf-tracking.aspx
• paper: https://ivul.kaust.edu.sa/Documents/Publications/2017/Context-Aware%20Correlation%20Filter%20Tracking.pdf
• github: https://github.com/thias15/Context-Aware-CF-Tracking

Robust Multi-view Pedestrian Tracking Using Neural Networks

https://arxiv.org/abs/1704.06370

Re3 : Real-Time Recurrent Regression Networks for Object Tracking

• intro: University of Washington
• arxiv: https://arxiv.org/abs/1705.06368
• demo: https://www.youtube.com/watch?v=PC0txGaYz2I

Robust Tracking Using Region Proposal Networks

https://arxiv.org/abs/1705.10447

Hierarchical Attentive Recurrent Tracking

• intro: NIPS 2017. University of Oxford
• arxiv: https://arxiv.org/abs/1706.09262
• github: https://github.com/akosiorek/hart
• results: https://youtu.be/Vvkjm0FRGSs

Siamese Learning Visual Tracking: A Survey

https://arxiv.org/abs/1707.00569

Robust Visual Tracking via Hierarchical Convolutional Features

• project page: https://sites.google.com/site/chaoma99/hcft-tracking
• arxiv: https://arxiv.org/abs/1707.03816
• github: https://github.com/chaoma99/HCFTstar

CREST: Convolutional Residual Learning for Visual Tracking

• intro: ICCV 2017
• project page: http://www.cs.cityu.edu.hk/~yibisong/iccv17/index.html
• arxiv: https://arxiv.org/abs/1708.00225
• github: https://github.com/ybsong00/CREST-Release

Learning Policies for Adaptive Tracking with Deep Feature Cascades

• intro: ICCV 2017 Spotlight
• arxiv: https://arxiv.org/abs/1708.02973

Recurrent Filter Learning for Visual Tracking

• intro: ICCV 2017 Workshop on VOT
• arxiv: https://arxiv.org/abs/1708.03874

Correlation Filters with Weighted Convolution Responses

• intro: ICCV 2017 workshop. 5th visual object tracking(VOT) tracker CFWCR
• paper: http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w28/He_Correlation_Filters_With_ICCV_2017_paper.pdf
• github: https://github.com/he010103/CFWCR

Semantic Texture for Robust Dense Tracking

https://arxiv.org/abs/1708.08844

Learning Multi-frame Visual Representation for Joint Detection and Tracking of Small Objects

Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects

https://arxiv.org/abs/1709.04666

Tracking Persons-of-Interest via Unsupervised Representation Adaptation

• intro: Northwestern Polytechnical University & Virginia Tech & Hanyang University
• keywords: Multi-face tracking
• project page: http://vllab1.ucmerced.edu/~szhang/FaceTracking/
• arxiv: https://arxiv.org/abs/1710.02139

End-to-end Flow Correlation Tracking with Spatial-temporal Attention

https://arxiv.org/abs/1711.01124

UCT: Learning Unified Convolutional Networks for Real-time Visual Tracking

• intro: ICCV 2017 Workshops
• arxiv: https://arxiv.org/abs/1711.04661

Pixel-wise object tracking

https://arxiv.org/abs/1711.07377

MAVOT: Memory-Augmented Video Object Tracking

https://arxiv.org/abs/1711.09414

Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks

https://arxiv.org/abs/1801.02021

Parallel Tracking and Verifying

https://arxiv.org/abs/1801.10496

Saliency-Enhanced Robust Visual Tracking

https://arxiv.org/abs/1802.02783

A Twofold Siamese Network for Real-Time Object Tracking

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1802.08817

Learning Dynamic Memory Networks for Object Tracking

https://arxiv.org/abs/1803.07268

Context-aware Deep Feature Compression for High-speed Visual Tracking

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1803.10537

VITAL: VIsual Tracking via Adversarial Learning

• intro: CVPR 2018 Spotlight
• arixv: https://arxiv.org/abs/1804.04273

Unveiling the Power of Deep Tracking

https://arxiv.org/abs/1804.06833

A Novel Low-cost FPGA-based Real-time Object Tracking System

• intro: ASICON 2017
• arxiv: https://arxiv.org/abs/1804.05535

MV-YOLO: Motion Vector-aided Tracking by Semantic Object Detection

https://arxiv.org/abs/1805.00107

Information-Maximizing Sampling to Promote Tracking-by-Detection

https://arxiv.org/abs/1806.02523

Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks

• intro: MICCAI 2018
• arxiv: https://arxiv.org/abs/1806.02070

Stochastic Channel Decorrelation Network and Its Application to Visual Tracking

https://arxiv.org/abs/1807.01103

Fast Dynamic Convolutional Neural Networks for Visual Tracking

https://arxiv.org/abs/1807.03132

DeepTAM: Deep Tracking and Mapping

https://arxiv.org/abs/1808.01900

Distractor-aware Siamese Networks for Visual Object Tracking

• intro: ECCV 2018
• keywords: DaSiamRPN
• arxiv: https://arxiv.org/abs/1808.06048
• github: https://github.com/foolwood/DaSiamRPN

Multi-Branch Siamese Networks with Online Selection for Object Tracking

• intro: ISVC 2018 oral
• arxiv: https://arxiv.org/abs/1808.07349

Real-Time MDNet

• intro: ECCV 2018
• arxiv: https://arxiv.org/abs/1808.08834

Towards a Better Match in Siamese Network Based Visual Object Tracker

• intro: ECCV Visual Object Tracking Challenge Workshop VOT2018
• arxiv: https://arxiv.org/abs/1809.01368

DensSiam: End-to-End Densely-Siamese Network with Self-Attention Model for Object Tracking

• intro: ISVC 2018
• arxiv: https://arxiv.org/abs/1809.02714

Deformable Object Tracking with Gated Fusion

https://arxiv.org/abs/1809.10417

Deep Attentive Tracking via Reciprocative Learning

• intro: NIPS 2018
• project page: https://ybsong00.github.io/nips18_tracking/index
• arxiv: https://arxiv.org/abs/1810.03851
• github: https://github.com/shipubupt/NIPS2018

Online Visual Robot Tracking and Identification using Deep LSTM Networks

• intro: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, 2017. IROS RoboCup Best Paper Award
• arxiv: https://arxiv.org/abs/1810.04941

Detect or Track: Towards Cost-Effective Video Object Detection/Tracking

• intro: AAAI 2019
• arxiv: https://arxiv.org/abs/1811.05340

Deep Siamese Networks with Bayesian non-Parametrics for Video Object Tracking

https://arxiv.org/abs/1811.07386

Fast Online Object Tracking and Segmentation: A Unifying Approach

• intro: CVPR 2019
• preject page: http://www.robots.ox.ac.uk/~qwang/SiamMask/
• arxiv: https://arxiv.org/abs/1812.05050
• github: https://github.com/foolwood/SiamMask

Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking

• intro: Temple University
• arxiv: https://arxiv.org/abs/1812.06148

Handcrafted and Deep Trackers: A Review of Recent Object Tracking Approaches

https://arxiv.org/abs/1812.07368

SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks

https://arxiv.org/abs/1812.11703

Deeper and Wider Siamese Networks for Real-Time Visual Tracking

https://arxiv.org/abs/1901.01660

SiamVGG: Visual Tracking using Deeper Siamese Networks

https://arxiv.org/abs/1902.02804

TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis

https://arxiv.org/abs/1902.01466

Target-Aware Deep Tracking

• intro: CVPR 2019
• intro: 1Harbin Institute of Technology & Shanghai Jiao Tong University & Tencent AI Lab & University of California & Google Cloud AI
• arxiv: https://arxiv.org/abs/1904.01772

Unsupervised Deep Tracking

• intro: CVPR 2019
• intro: USTC & Tencent AI Lab & Shanghai Jiao Tong University
• arxiv: https://arxiv.org/abs/1904.01828
• github: https://github.com/594422814/UDT
• github: https://github.com/594422814/UDT_pytorch

Generic Multiview Visual Tracking

https://arxiv.org/abs/1904.02553

SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking

• intro: CVPR 2019
• arxiv: https://arxiv.org/abs/1904.04452

A Strong Feature Representation for Siamese Network Tracker

https://arxiv.org/abs/1907.07880

Visual Tracking via Dynamic Memory Networks

• intro: TPAMI 2019
• arxiv: https://arxiv.org/abs/1907.07613

Multi-Adapter RGBT Tracking

• arxiv: https://arxiv.org/abs/1907.07485
• github: https://github.com/Alexadlu/MANet

Teacher-Students Knowledge Distillation for Siamese Trackers

https://arxiv.org/abs/1907.10586

Tell Me What to Track

• intro: Boston University & Horizon Robotics & University of Chinese Academy of Sciences
• arxiv: https://arxiv.org/abs/1907.11751

Learning to Track Any Object

• intro: ICCV 2019 Holistic Video Understanding workshop
• arxiv: https://arxiv.org/abs/1910.11844

ROI Pooled Correlation Filters for Visual Tracking

• intro: CVPR 2019
• arxiv: https://arxiv.org/abs/1911.01668

D3S – A Discriminative Single Shot Segmentation Tracker

• intro: CVPR 2020
• arxiv: https://arxiv.org/abs/1911.08862
• github(PyTorch): https://github.com/alanlukezic/d3s

Visual Tracking by TridentAlign and Context Embedding

• arxiv: https://arxiv.org/abs/2007.06887
• github: https://github.com/JanghoonChoi/TACT

Transformer Tracking

• intro: CVPR 2021
• intro: Dalian University of Technology & Peng Cheng Laboratory & Remark AI
• arxiv: https://arxiv.org/abs/2103.15436
• github: https://github.com/chenxin-dlut/TransT

Face Tracking

Mobile Face Tracking: A Survey and Benchmark

https://arxiv.org/abs/1805.09749

Multi-Object Tracking (MOT)

Simple Online and Realtime Tracking

• intro: ICIP 2016
• arxiv: https://arxiv.org/abs/1602.00763
• github: https://github.com/abewley/sort

Simple Online and Realtime Tracking with a Deep Association Metric

• intro: ICIP 2017
• arxiv: https://arxiv.org/abs/1703.07402
• mot challenge: https://motchallenge.net/tracker/DeepSORT_2
• github(official, Python): https://github.com/nwojke/deep_sort
• github(C++): https://github.com/oylz/ds

StrongSORT: Make DeepSORT Great Again

• intro: Beijing University of Posts and Telecommunications & Xidian University
• arxiv: https://arxiv.org/abs/2202.13514

Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking

• intro: Carnegie Mellon University & The Chinese University of Hong Kong & Shanghai AI Laboratory
• arxiv: https://arxiv.org/abs/2203.14360
• github: https://github.com/noahcao/OC_SORT

BoT-SORT: Robust Associations Multi-Pedestrian Tracking

• intro: Tel-Aviv University
• arxiv: https://arxiv.org/abs/2206.14651

Virtual Worlds as Proxy for Multi-Object Tracking Analysis

• arxiv: http://arxiv.org/abs/1605.06457
• dataset(Virtual KITTI): http://www.xrce.xerox.com/Research-Development/Computer-Vision/Proxy-Virtual-Worlds

Multi-Class Multi-Object Tracking using Changing Point Detection

• intro: changing point detection, entity transition, object detection from video, convolutional neural network
• arxiv: http://arxiv.org/abs/1608.08434

POI: Multiple Object Tracking with High Performance Detection and Appearance Feature

• intro: ECCV workshop BMTT 2016. Sensetime
• keywords: KDNT
• arxiv: https://arxiv.org/abs/1610.06136

Multiple Object Tracking: A Literature Review

• intro: last revised 22 May 2017 (this version, v4)
• arxiv: https://arxiv.org/abs/1409.7618

Deep Network Flow for Multi-Object Tracking

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1706.08482

Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism

https://arxiv.org/abs/1708.02843

Recurrent Autoregressive Networks for Online Multi-Object Tracking

https://arxiv.org/abs/1711.02741

SOT for MOT

• intro: Tsinghua University & Megvii Inc. (Face++)
• arxiv: https://arxiv.org/abs/1712.01059

Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project

https://arxiv.org/abs/1712.09531

Multiple Target Tracking by Learning Feature Representation and Distance Metric Jointly

https://arxiv.org/abs/1802.03252

Tracking Noisy Targets: A Review of Recent Object Tracking Approaches

https://arxiv.org/abs/1802.03098

Machine Learning Methods for Solving Assignment Problems in Multi-Target Tracking

• intro: University of Florida
• arxiv: https://arxiv.org/abs/1802.06897

Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World

• intro: University of Modena and Reggio Emilia
• arxiv: https://arxiv.org/abs/1803.08319

Features for Multi-Target Multi-Camera Tracking and Re-Identification

• intro: CVPR 2018 spotlight
• intro: https://arxiv.org/abs/1803.10859

High Performance Visual Tracking with Siamese Region Proposal Network

• intro: CVPR 2018 spotlight
• keywords: SiamRPN
• paper: http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_High_Performance_Visual_CVPR_2018_paper.pdf
• slides: https://drive.google.com/file/d/1OGIOUqANvYfZjRoQfpiDqhPQtOvPCpdq/view

Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking

• intro: Peking University
• arxiv: https://arxiv.org/abs/1804.04555

Automatic Adaptation of Person Association for Multiview Tracking in Group Activities

• intro: Carnegie Mellon University & Argo AI & Adobe Research
• project page: http://www.cs.cmu.edu/~ILIM/projects/IM/Association4Tracking/
• arxiv: https://arxiv.org/abs/1805.08717

Improving Online Multiple Object tracking with Deep Metric Learning

https://arxiv.org/abs/1806.07592

Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking

• intro: Tsinghua Univeristy & Horizon Robotics
• arxiv: https://arxiv.org/abs/1808.01562

Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector

• intro: 13th International Symposium on Visual Computing (ISVC)
• arxiv: https://arxiv.org/abs/1809.02073

Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers

https://arxiv.org/abs/1809.03137

Deep Affinity Network for Multiple Object Tracking

• intro: IEEE TPAMI 2018
• arxiv: https://arxiv.org/abs/1810.11780
• github: https://github.com/shijieS/SST

Exploit the Connectivity: Multi-Object Tracking with TrackletNet

https://arxiv.org/abs/1811.07258

Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification

• intro: Sensetime Group Limited & Beihang University & The University of Sydney
• arxiv: https://arxiv.org/abs/1901.06129

Online Multi-Object Tracking with Dual Matching Attention Networks

• intro: ECCV 2018
• arxiv: https://arxiv.org/abs/1902.00749

Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment

https://arxiv.org/abs/1902.08231

Tracking without bells and whistles

• intro: Technical University of Munich
• keywords: Tracktor
• arxiv: https://arxiv.org/abs/1903.05625
• github: https://github.com/phil-bergmann/tracking_wo_bnw

Spatial-Temporal Relation Networks for Multi-Object Tracking

• intro: Hong Kong University of Science and Technology & Tsinghua University & MSRA
• arxiv: https://arxiv.org/abs/1904.11489

Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking

• intro: Baidu X-Lab & UC Irvine
• arxiv: https://arxiv.org/abs/1905.11026

State-aware Re-identification Feature for Multi-target Multi-camera Tracking

• intro: CVPR-2019 TRMTMCT Workshop
• intro: BUPT & Chinese Academy of Sciences & Horizon Robotics
• arxiv: https://arxiv.org/abs/1906.01357

DeepMOT: A Differentiable Framework for Training Multiple Object Trackers

• intro: Inria
• keywords: deep Hungarian network (DHN)
• arxiv: https://arxiv.org/abs/1906.06618
• gitlab: https://gitlab.inria.fr/yixu/deepmot

Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking

• intro: Beihang University && Inception Institute of Artificial Intelligence
• arxiv: https://arxiv.org/abs/1907.05315

End-to-End Learning Deep CRF models for Multi-Object Tracking

https://arxiv.org/abs/1907.12176

End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning

• intro: University of Oxford
• arxiv: https://arxiv.org/abs/1907.12887

Robust Multi-Modality Multi-Object Tracking

• intro: ICCV 2019
• keywords: LiDAR
• arxiv: https://arxiv.org/abs/1909.03850
• github: https://github.com/ZwwWayne/mmMOT

Learning Multi-Object Tracking and Segmentation from Automatic Annotations

https://arxiv.org/abs/1912.02096

Learning a Neural Solver for Multiple Object Tracking

• intro: Technical University of Munich
• keywords: Message Passing Networks (MPNs)
• arxiv: https://arxiv.org/abs/1912.07515
• github: https://github.com/dvl-tum/mot_neural_solver

Multi-object Tracking via End-to-end Tracklet Searching and Ranking

• intro: Horizon Robotics Inc
• arxiv: https://arxiv.org/abs/2003.02795

Refinements in Motion and Appearance for Online Multi-Object Tracking

https://arxiv.org/abs/2003.07177

A Unified Object Motion and Affinity Model for Online Multi-Object Tracking

• intro: CVPR 2020
• arxiv: https://arxiv.org/abs/2003.11291
• github: https://github.com/yinjunbo/UMA-MOT

A Simple Baseline for Multi-Object Tracking

• intro: Microsoft Research Asia
• arxiv: https://arxiv.org/abs/2004.01888
• github: https://github.com/ifzhang/FairMOT

MOPT: Multi-Object Panoptic Tracking

• intro: University of Freiburg
• arxiv: https://arxiv.org/abs/2004.08189

SQE: a Self Quality Evaluation Metric for Parameters Optimization in Multi-Object Tracking

• intro: Tsinghua University & Megvii Inc
• arxiv: https://arxiv.org/abs/2004.07472

Multi-Object Tracking with Siamese Track-RCNN

• intro: Amazon Web Service (AWS) Rekognition
• arixv: https://arxiv.org/abs/2004.07786

TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model

• intro: CVPR 2020 oral
• arxiv: https://arxiv.org/abs/2006.05683
• github: https://github.com/BoPang1996/TubeTK

Quasi-Dense Similarity Learning for Multiple Object Tracking

• intro: CVPR 2021 oral
• intro: Zhejiang University & Georgia Institute of Technology & ETH Zürich & Stanford University & UC Berkeley
• project page: https://www.vis.xyz/pub/qdtrack/
• arxiv: https://arxiv.org/abs/2006.06664
• github: https://github.com/SysCV/qdtrack

imultaneous Detection and Tracking with Motion Modelling for Multiple Object Tracking

• intro: ECCV 2020
• arxiv: https://arxiv.org/abs/2008.08826
• github: https://github.com/shijieS/DMMN

MAT: Motion-Aware Multi-Object Tracking

https://arxiv.org/abs/2009.04794

SAMOT: Switcher-Aware Multi-Object Tracking and Still Another MOT Measure

https://arxiv.org/abs/2009.10338

GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization

• intro: Virginia Tech
• arxiv: https://arxiv.org/abs/2010.00067

Rethinking the competition between detection and ReID in Multi-Object Tracking

• intro: University of Electronic Science and Technology of China(UESTC) & Chinese Academy of Sciences
• arxiv: https://arxiv.org/abs/2010.12138

GMOT-40: A Benchmark for Generic Multiple Object Tracking

• intro: Temple University & Stony Brook University & Microsoft
• arxiv: https://arxiv.org/abs/2011.11858

Multi-object Tracking with a Hierarchical Single-branch Network

https://arxiv.org/abs/2101.01984

Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking

• intro: Georgia Institute of Technology & Oregon State University
• arxiv: https://arxiv.org/abs/2101.12159

Learning a Proposal Classifier for Multiple Object Tracking

• intro: CVPR 2021 poster
• arxiv: https://arxiv.org/abs/2103.07889
• github: https://github.com/daip13/LPC_MOT

Track to Detect and Segment: An Online Multi-Object Tracker

• intro: CVPR 2021
• intro: SUNY Buffalo & TJU & Horizon Robotics
• project page: https://jialianwu.com/projects/TraDeS.html
• arxiv: https://arxiv.org/abs/2103.08808

Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking

• intro: CVPR 2021
• arxiv: https://arxiv.org/abs/2103.16178
• github: https://github.com/jiaweihe1996/GMTracker

Multiple Object Tracking with Correlation Learning

• intro: CVPR 2021
• intro: Machine Intelligence Technology Lab, Alibaba Group
• arxiv: https://arxiv.org/abs/2104.03541

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

• intro: Huazhong University of Science and Technology & The University of Hong Kong & ByteDance
• arxiv: https://arxiv.org/abs/2110.06864
• github: https://github.com/ifzhang/ByteTrack

SiamMOT: Siamese Multi-Object Tracking

• intro: Amazon Web Services (AWS)
• arxiv: https://arxiv.org/abs/2105.11595
• github: https://github.com/amazon-research/siam-mot

Synthetic Data Are as Good as the Real for Association Knowledge Learning in Multi-object Tracking

• arxiv: https://arxiv.org/abs/2106.16100
• github: https://github.com/liuyvchi/MOTX

Track to Detect and Segment: An Online Multi-Object Tracker

• intro: CVPR 2021
• intro: SUNY Buffalo & TJU & Horizon Robotics
• project page: https://jialianwu.com/projects/TraDeS.html
• arxiv: https://arxiv.org/abs/2103.08808
• github: https://github.com/JialianW/TraDeS

Learning of Global Objective for Network Flow in Multi-Object Tracking

• intro: CVPR 2022
• intro: Rochester Institute of Technology & Monash University
• arxiv: https://arxiv.org/abs/2203.16210

MeMOT: Multi-Object Tracking with Memory

• intro: CVPR 2022 Oral
• arxiv: https://arxiv.org/abs/2203.16761

TR-MOT: Multi-Object Tracking by Reference

• intro: University of Washington & Beihang University & SenseTime Research
• arxiv: https://arxiv.org/abs/2203.16621

Towards Grand Unification of Object Tracking

• intro: ECCV 2022 Oral
• intro: Dalian University of Technology & ByteDance & The University of Hong Kong
• arxiv: https://arxiv.org/abs/2207.07078
• github: https://github.com/MasterBin-IIAU/Unicorn

Tracking Every Thing in the Wild

• intro: ECCV 2022
• intro: Computer Vision Lab, ETH Zürich
• project page: https://www.vis.xyz/pub/tet/
• arxiv: https://arxiv.org/abs/2207.12978
• github: https://github.com/SysCV/tet

Transformer

TransTrack: Multiple-Object Tracking with Transformer

• intro: The University of Hong Kong & ByteDance AI Lab & Tongji University & Carnegie Mellon University & Nanyang Technological University
• arxiv: https://arxiv.org/abs/2012.15460
• github: https://github.com/PeizeSun/TransTrack

TrackFormer: Multi-Object Tracking with Transformers

• intro: Technical University of Munich & Facebook AI Research (FAIR)
• arxiv: https://arxiv.org/abs/2101.02702

TransCenter: Transformers with Dense Queries for Multiple-Object Tracking

• intro: Inria & MIT & MIT-IBM Watson AI Lab
• arxiv: https://arxiv.org/abs/2103.15145

Looking Beyond Two Frames: End-to-End Multi-Object Tracking UsingSpatial and Temporal Transformers

• intro: Monash University & The University of Adelaide & Australian Centre for Robotic Vision
• arixiv: https://arxiv.org/abs/2103.14829

TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking

• intro: Microsoft & StonyBrook University
• arxiv: https://arxiv.org/abs/2104.00194

MOTR: End-to-End Multiple-Object Tracking with TRansformer

• intro: MEGVII Technology
• arxiv: https://arxiv.org/abs/2105.03247
• github: https://github.com/megvii-model/MOTR

Global Tracking Transformers

• intro: CVPR 2022
• intro: The University of Texas at Austin & Apple
• arxiv: https://arxiv.org/abs/2203.13250
• github: https://github.com/xingyizhou/GTR

Multiple People Tracking

Multi-Person Tracking by Multicut and Deep Matching

• intro: Max Planck Institute for Informatics
• arxiv: http://arxiv.org/abs/1608.05404

Joint Flow: Temporal Flow Fields for Multi Person Tracking

• intro: University of Bonn
• arxiv: https://arxiv.org/abs/1805.04596

Multiple People Tracking by Lifted Multicut and Person Re-identification

• intro: CVPR 2017
• intro: Max Planck Institute for Informatics & Max Planck Institute for Intelligent Systems
• paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Tang_Multiple_People_Tracking_CVPR_2017_paper.pdf
• code: https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/people-detection-pose-estimation-and-tracking/multiple-people-tracking-with-lifted-multicut-and-person-re-identification/

Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking

• intro: WACV 2018
• intro: Queensland University of Technology (QUT)
• arxiv: https://arxiv.org/abs/1803.03347

Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification

• intro: ICME 2018
• arxiv: https://arxiv.org/abs/1809.04427
• github: https://github.com/longcw/MOTDT

Deep Person Re-identification for Probabilistic Data Association in Multiple Pedestrian Tracking

https://arxiv.org/abs/1810.08565

Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification

https://arxiv.org/abs/1811.04091

Multi-person Articulated Tracking with Spatial and Temporal Embeddings

• intro: CVPR 2019
• intro: SenseTime Research & The University of Sydney & SenseTime Computer Vision Research Group
• arxiv: https://arxiv.org/abs/1903.09214

Instance-Aware Representation Learning and Association for Online Multi-Person Tracking

• intro: Pattern Recognition
• intro: Sun Yat-sen University & Guangdong University of Foreign Studies & Carnegie Mellon University & University of California & Guilin University of Electronic Technology & WINNER Technology
• arxiv: https://arxiv.org/abs/1905.12409

Online Multiple Pedestrian Tracking using Deep Temporal Appearance Matching Association

• intro: 2nd ranked tracker of the MOTChallenge on CVPR19 workshop
• arxiv: https://arxiv.org/abs/1907.00831

Detecting Invisible People

• intro: Carnegie Mellon University & Argo AI
• project page: http://www.cs.cmu.edu/~tkhurana/invisible.htm
• arxiv: https://arxiv.org/abs/2012.08419

MOTS

MOTS: Multi-Object Tracking and Segmentation

• intro: CVPR 2019
• intro: RWTH Aachen University
• keywords: TrackR-CNN
• project page: https://www.vision.rwth-aachen.de/page/mots
• arxiv: https://arxiv.org/abs/1902.03604
• github(official): https://github.com/VisualComputingInstitute/TrackR-CNN

Segment as Points for Efficient Online Multi-Object Tracking and Segmentation

• intro: ECCV 2020 oral
• intro: PointTrack
• arxiv: https://arxiv.org/abs/2007.01550
• github: https://github.com/detectRecog/PointTrack

PointTrack++ for Effective Online Multi-Object Tracking and Segmentation

• intro: CVPR2020 MOTS Challenge Winner. PointTrack++ ranks first on KITTI MOTS
• arxiv: https://arxiv.org/abs/2007.01549

Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation

• intro: NeurIPS 2021 Spotlight
• intro: ETH Zürich & HKUST & Kuaishou Technology
• keywords: Prototypical Cross-Attention Networks (PCAN)
• project page: https://www.vis.xyz/pub/pcan/
• arxiv: https://arxiv.org/abs/2106.11958
• github: https://github.com/SysCV/pcan
• youtube: https://www.youtube.com/watch?v=hhAC2H0fmP8
• bilibili: https://www.bilibili.com/video/av593811548
• zhihu: https://zhuanlan.zhihu.com/p/445457150

Multi-Object Tracking and Segmentation with a Space-Time Memory Network

• intro: Polytechnique Montreal
• project page: http://www.mehdimiah.com/mentos+
• arxiv: https://arxiv.org/abs/2110.11284

Multi-target multi-camera tracking (MTMCT)

Traffic-Aware Multi-Camera Tracking of Vehicles Based on ReID and Camera Link Model

• intro: ACMMM 2020
• arxiv: https://arxiv.org/abs/2008.09785

3D MOT

A Baseline for 3D Multi-Object Tracking

• arxiv: https://arxiv.org/abs/1907.03961
• github: https://github.com/xinshuoweng/AB3DMOT

Probabilistic 3D Multi-Object Tracking for Autonomous Driving

• intro: NeurIPS 2019
• intro: 1st Place Award, NuScenes Tracking Challenge
• intro: Stanford University $ Toyota Research Institute
• arxiv: https://arxiv.org/abs/2001.05673
• github: https://github.com/eddyhkchiu/mahalanobis_3d_multi_object_tracking

JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset

• intro: Stanford University
• arxiv: https://arxiv.org/abs/2002.08397
• github: https://github.com/StanfordVL/JRMOT_ROS

Real-time 3D Deep Multi-Camera Tracking

• intro: Microsoft Cloud & AI
• arxiv: https://arxiv.org/abs/2003.11753

P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds

• intro: CVPR 2020 oral
• intro: Huazhong University of Science and Technology
• arxiv: https://arxiv.org/abs/2005.13888
• github: https://github.com/HaozheQi/P2B

PnPNet: End-to-End Perception and Prediction with Tracking in the Loop

• intro: CVPR 2020
• intro: Uber Advanced Technologies Group & University of Toronto
• arxiv: https://arxiv.org/abs/2005.14711

GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning

• intro: CVPR 2020
• intro: Carnegie Mellon University
• arxiv: https://arxiv.org/abs/2006.07327
• github(official, PyTorch): https://github.com/xinshuoweng/GNN3DMOT

1st Place Solutions for Waymo Open Dataset Challenges – 2D and 3D Tracking

• intro: Horizon Robotics Inc.
• arxiv: https://arxiv.org/abs/2006.15506

Graph Neural Networks for 3D Multi-Object Tracking

• intro: ECCV 2020 workshop
• intro: Robotics Institute, Carnegie Mellon University
• project page: http://www.xinshuoweng.com/projects/GNN3DMOT/
• arxiv: https://arxiv.org/abs/2008.09506
• github: https://github.com/xinshuoweng/GNN3DMOT

Learnable Online Graph Representations for 3D Multi-Object Tracking

https://arxiv.org/abs/2104.11747

SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking

• intro: UIUC & TuSimple
• arxiv: https://arxiv.org/abs/2111.09621
• github: https://github.com/TuSimple/SimpleTrack

Immortal Tracker: Tracklet Never Dies

• intro: University of Chinese Academy of Sciences & Tusimple & CASIA & UIUC
• arxiv: https://arxiv.org/abs/2111.13672
• github: https://github.com/ImmortalTracker/ImmortalTracker

Single Stage Joint Detection and Tracking

Bridging the Gap Between Detection and Tracking: A Unified Approach

• intro: ICCV 2019
• paper: https://openaccess.thecvf.com/content_ICCV_2019/papers/Huang_Bridging_the_Gap_Between_Detection_and_Tracking_A_Unified_Approach_ICCV_2019_paper.pdf

Towards Real-Time Multi-Object Tracking

• intro: Tsinghua University & Austrilian National University
• arxiv: https://arxiv.org/abs/1909.12605
• github: https://github.com/Zhongdao/Towards-Realtime-MOT

RetinaTrack: Online Single Stage Joint Detection and Tracking

• intro: CVPR 2020
• intro: Google
• arxiv: https://arxiv.org/abs/2003.13870

Tracking Objects as Points

• intro: UT Austin & Intel Labs
• intro: Simultaneous object detection and tracking using center points.
• keywords: CenterTrack
• arxiv: https://arxiv.org/abs/2004.01177
• github: https://github.com/xingyizhou/CenterTrack

Fully Convolutional Online Tracking

• intro: Nanjing University
• arxiv: https://arxiv.org/abs/2004.07109
• github(official, PyTorch): https://github.com/MCG-NJU/FCOT

Accurate Anchor Free Tracking

• intro: Tongji University & UCLA
• keywords: Anchor Free Siamese Network (AFSN)
• arxiv: https://arxiv.org/abs/2006.07560

Ocean: Object-aware Anchor-free Tracking

• intro: ECCV 2020
• intro: NLPR, CASIA & UCAS & Microsoft Research
• arxiv: https://arxiv.org/abs/2006.10721
• github: https://github.com/researchmm/TracKit

Joint Detection and Multi-Object Tracking with Graph Neural Networks

• intro: Carnegie Mellon University
• arxiv: https://arxiv.org/abs/2006.13164

Joint Multiple-Object Detection and Tracking

Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking

• intro: ECCV 2020 spotlight
• intro: Tencent Youtu Lab & Fudan University & Nara Institute of Science and Technology
• keywords: Chained-Tracker (CTracker)
• arxiv: https://arxiv.org/abs/2007.14557
• github: https://github.com/pjl1995/CTracker

SMOT: Single-Shot Multi Object Tracking

https://arxiv.org/abs/2010.16031

DEFT: Detection Embeddings for Tracking

• arxiv: https://arxiv.org/abs/2102.02267
• github: https://github.com/MedChaabane/DEFT

Global Correlation Network: End-to-End Joint Multi-Object Detection and Tracking

• intro: ICCV 2021
• intro: Intell Tsinghua University & Beihang University
• arxiv: https://arxiv.org/abs/2103.12511

Tracking with Reinforcement Learning

Deep Reinforcement Learning for Visual Object Tracking in Videos

• intro: University of California at Santa Barbara & Samsung Research America
• arxiv: https://arxiv.org/abs/1701.08936

Visual Tracking by Reinforced Decision Making

• arxiv: https://arxiv.org/abs/1702.06291

End-to-end Active Object Tracking via Reinforcement Learning

https://arxiv.org/abs/1705.10561

Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning

• project page: https://sites.google.com/view/cvpr2017-adnet
• paper: https://drive.google.com/file/d/0B34VXh5mZ22cZUs2Umc1cjlBMFU/view?usp=drive_web

Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning

https://arxiv.org/abs/1707.04991

Detect to Track and Track to Detect

• intro: ICCV 2017
• project page: https://www.robots.ox.ac.uk/~vgg/research/detect-track/
• arxiv: https://arxiv.org/abs/1710.03958
• github: https://github.com/feichtenhofer/Detect-Track

Projects

MMTracking

• intro: OpenMMLab Video Perception Toolbox. It supports Single Object Tracking (SOT), Multiple Object Tracking (MOT), Video Object Detection (VID) with a unified framework.
• github: https://github.com/open-mmlab/mmtracking

Tensorflow_Object_Tracking_Video

• intro: Object Tracking in Tensorflow ( Localization Detection Classification ) developed to partecipate to ImageNET VID competition
• github: https://github.com/DrewNF/Tensorflow_Object_Tracking_Video

Resources

Multi-Object-Tracking-Paper-List

• intro: Paper list and source code for multi-object-tracking
• github: https://github.com/SpyderXu/multi-object-tracking-paper-list

Papers