7 papers
RELO: Reinforcement Learning to Localize for Visual Object Tracking
Xin Chen, Chuanyu Sun, Jiao Xu +4
Conventional visual object trackers localize targets using handcrafted spatial priors, often in the form of heatmaps. Such priors provide only surrogate supervision and are poorly…
UETrack: A Unified and Efficient Framework for Single Object Tracking
Ben Kang, Jie Zhao, Xin Chen +5
With growing real-world demands, efficient tracking has received increasing attention. However, most existing methods are limited to RGB inputs and struggle in multi-modal scenario…
Efficient Motion Prompt Learning for Robust Visual Tracking
Jie Zhao, Xin Chen, Yongsheng Yuan +3
Due to the challenges of processing temporal information, most trackers depend solely on visual discriminability and overlook the unique temporal coherence of video data. In this p…
Exploring Dynamic Transformer for Efficient Object Tracking
Jiawen Zhu, Xin Chen, Haiwen Diao +6
The speed-precision trade-off is a critical problem for visual object tracking which usually requires low latency and deployment on constrained resources. Existing solutions for ef…
Two-stream Beats One-stream: Asymmetric Siamese Network for Efficient Visual Tracking
Jiawen Zhu, Huayi Tang, Xin Chen +3
Efficient tracking has garnered attention for its ability to operate on resource-constrained platforms for real-world deployment beyond desktop GPUs. Current efficient trackers mai…
SUTrack: Towards Simple and Unified Single Object Tracking
Xin Chen, Ben Kang, Wanting Geng +4
In this paper, we propose a simple yet unified single object tracking (SOT) framework, dubbed SUTrack. It consolidates five SOT tasks (RGB-based, RGB-Depth, RGB-Thermal, RGB-Event,…