Tracking

Published: 09 Oct 2015 Category: deep_learning

DLT

Learning A Deep Compact Image Representation for Visual Tracking

Hierarchical Convolutional Features for Visual Tracking

Robust Visual Tracking via Convolutional Networks

SO-DLT

Transferring Rich Feature Hierarchies for Robust Visual Tracking

MDNet

Learning Multi-Domain Convolutional Neural Networks for Visual Tracking

RATM: Recurrent Attentive Tracking Model

Understanding and Diagnosing Visual Tracking Systems

Recurrently Target-Attending Tracking

Visual Tracking with Fully Convolutional Networks

Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks

Learning to Track at 100 FPS with Deep Regression Networks

Learning by tracking: Siamese CNN for robust target association

Virtual Worlds as Proxy for Multi-Object Tracking Analysis

Fully-Convolutional Siamese Networks for Object Tracking

Hedged Deep Tracking

ROLO

Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking

Visual Tracking via Shallow and Deep Collaborative Model

Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

Predictive Vision Model (PVM)

Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network

Multi-Person Tracking by Multicut and Deep Matching

Modeling and Propagating CNNs in a Tree Structure for Visual Tracking

Multi-Class Multi-Object Tracking using Changing Point Detection

Robust Scale Adaptive Kernel Correlation Filter Tracker With Hierarchical Convolutional Features

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

OTB Results: visual tracker benchmark results

Convolutional Regression for Visual Tracking

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

SANet: Structure-Aware Network for Visual Tracking

ECO: Efficient Convolution Operators for Tracking

Dual Deep Network for Visual Tracking

Deep Motion Features for Visual Tracking

Globally Optimal Object Tracking with Fully Convolutional Networks

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

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

Deep Reinforcement Learning for Visual Object Tracking in Videos

Visual Tracking by Reinforced Decision Making

Large Margin Object Tracking with Circulant Feature Maps

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

Robust Multi-view Pedestrian Tracking Using Neural Networks

https://arxiv.org/abs/1704.06370

Re3 : Real-Time Recurrent Regression Networks for Object Tracking