[CVPR19/TPAMI23] SiamMask: A Framework for Fast Online Object Tracking and Segmentation
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Updated
Feb 14, 2025 - Python
[CVPR19/TPAMI23] SiamMask: A Framework for Fast Online Object Tracking and Segmentation
Visual tracking library based on PyTorch.
(2020-2022)The PyTorch version of SiamFC,SiamRPN,DaSiamRPN, UpdateNet , SiamDW, SiamRPN++, SiamMask, SiamFC++, SiamCAR, SiamBAN, Ocean, LightTrack , TrTr, NanoTrack; Visual object tracking based on deep learning
Official repo for FEAR: Fast, Efficient, Accurate and Robust Visual Tracker (ECCV 2022)
The official VOT Challenge evaluation and analysis toolkit
[CVPR-2024] The First High Definition (HD) Event based Visual Object Tracking Benchmark Dataset
[ECCV'22] The official PyTorch implementation of our ECCV 2022 paper: "AiATrack: Attention in Attention for Transformer Visual Tracking".
Open source visual object tracking library in python
[CVPR'23] The official PyTorch implementation of our CVPR 2023 paper: "Generalized Relation Modeling for Transformer Tracking".
[NeurIPS 2024] VastTrack: Vast Category Visual Object Tracking
Official Implementation of Towards Sequence-Level Training for Visual Tracking (ECCV 2022)
Visual Object Tracking algorithms. Hold on! There is a lot to come
[RA-L] Lost & Found dynamically tracks object poses from egocentric videos while updating a scene graph, enabling richer semantic 3D understanding for robotic downstream tasks.
[WACV 2024] Separable Self and Mixed Attention Transformers for Efficient Object Tracking
Visual Tracking by TridenAlign and Context Embedding
[BMVC 2023] Mobile Vision Transformer-based Visual Object Tracking
code and results for 'Effective Local and Global Search for Fast Long-term Tracking'
[ACCV 2020 / ICCVW 2019] 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).
The official implementation of the ECCV 2024 paper "Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL"
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