visual-tracking

CVPR 2021 论文和开源项目合集

Visual Tracking Paper List

Visual tracking library based on PyTorch.

Tracking Benchmark for Correlation Filters

Matlab implementation of the ECO tracker.

(2020-2021)The PyTorch version of SiamFC，SiamRPN，DaSiamRPN, UpdateNet , SiamDW, SiamRPN++, SiamMask, SiamFC++, SiamCar, SiamBAN, Ocean, LightTrack , TrTr; Visual object tracking based on deep learning

Visual Object Tracking (VOT) challenge evaluation toolkit

Python re-implementation of some correlation filter based tracker

PyTorch implementation of GOTURN object tracker: Learning to Track at 100 FPS with Deep Regression Networks (ECCV 2016)

2019-2020年目标跟踪资源全汇总（论文、模型代码、优秀实验室）极市团队整理

Open source visual object tracking library in python

C++ Implementation of SiamMask

The official VOT evaluation toolkit

Reference implementation of the Visual Tracking eXchange protocol.

Official implementation of "Energy-Based Models for Deep Probabilistic Regression" (ECCV 2020) and "How to Train Your Energy-Based Model for Regression" (BMVC 2020).

Implementation of MOSSE tracker in MATLAB: Visual Object Tracking using Adaptive Correlation Filters (CVPR 2010)

Kernel Cross-Correlator (KCC) for Tracking and Recognition (AAAI 2018)

Synthetic data generation for end-to-end TIR tracking (TIP2018)

Visual Tracking by TridenAlign and Context Embedding

Visual Object Tracking algorithms. Hold on! There is a lot to come

Real-time visual object tracking using correlations filters and deep learning

[ECCVW2018] A Memory Model based on the Siamese Network for Long-term Tracking (MMLT)

Matlab code for several visual tracking algorithms

Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark (CVPR 2021)

A collection of trackers' code for Visual Tracker Benchmark (OTB).

Official code implementation of the papers "Tracking-by-Trackers with a Distilled and Reinforced Model" (ACCV 2020) and "Visual Tracking by means of Deep Reinforcement Learning and an Expert Demonstrator" (ICCVW 2019).

This Repo include some paperlist and code with visual tracking based on deep reinforcement。

This work proposes a feature refined end-to-end tracking framework with a balanced performance using a high-level feature refine tracking framework. The feature refine module enhances the target feature representation power that allows the network to capture salient information to locate the target. The attention module is employed inside the feature refine mechanism to improve network discrimination power that augments the network ability to track the target in challenging scenarios.

Multi Agent Visual Tracking Project is based on the CamShift Algorithm on the Platform of MultiAgent Control