Published: 09 Oct 2015 Category: deep_learning


Learning A Deep Compact Image Representation for Visual Tracking

Hierarchical Convolutional Features for Visual Tracking

Robust Visual Tracking via Convolutional Networks


Transferring Rich Feature Hierarchies for Robust Visual Tracking


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

Fully-Convolutional Siamese Networks for Object Tracking

Hedged Deep Tracking


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

Modeling and Propagating CNNs in a Tree Structure for Visual Tracking

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

Large Margin Object Tracking with Circulant Feature Maps

DCFNet: Discriminant Correlation Filters Network for Visual Tracking

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

Context-Aware Correlation Filter Tracking

Robust Multi-view Pedestrian Tracking Using Neural Networks

Re3 : Real-Time Recurrent Regression Networks for Object Tracking

Robust Tracking Using Region Proposal Networks

Hierarchical Attentive Recurrent Tracking

Siamese Learning Visual Tracking: A Survey

Robust Visual Tracking via Hierarchical Convolutional Features

CREST: Convolutional Residual Learning for Visual Tracking

Learning Policies for Adaptive Tracking with Deep Feature Cascades

Recurrent Filter Learning for Visual Tracking

Semantic Texture for Robust Dense Tracking

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

Tracking Persons-of-Interest via Unsupervised Representation Adaptation

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

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

Pixel-wise object tracking

MAVOT: Memory-Augmented Video Object Tracking

Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks

Parallel Tracking and Verifying

Multi-Object Tracking (MOT)

Virtual Worlds as Proxy for Multi-Object Tracking Analysis

Multi-Person Tracking by Multicut and Deep Matching

Multi-Class Multi-Object Tracking using Changing Point Detection

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

Simple Online and Realtime Tracking with a Deep Association Metric

Deep Network Flow for Multi-Object Tracking

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

Recurrent Autoregressive Networks for Online Multi-Object Tracking


Tracking with Reinforcement Learning

Deep Reinforcement Learning for Visual Object Tracking in Videos

Visual Tracking by Reinforced Decision Making

End-to-end Active Object Tracking via Reinforcement Learning

Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning

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

Detect to Track and Track to Detect