Autoregressive Visual Tracking

Xing Wei, Yifan Bai, Yongchao Zheng, Dahu Shi, Yihong Gong
2/14/2026

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Abstract

We present ARTrack, an autoregressive framework for visual object tracking. ARTrack tackles tracking as a coordinate sequence interpretation task that estimates object trajectories progressively, where the current estimate is induced by previous states and in turn affects subsequences. This time-autoregressive approach models the sequential evolution of trajectories to keep tracing the object across frames, making it superior to existing template matching based trackers that only consider the per-frame localization accuracy. ARTrack is simple and direct, eliminating customized localization heads and post-processings. Despite its simplicity, ARTrack achieves state-of-the-art performance on prevailing benchmark datasets. Source code is available at https://github.com/MIV-XJTU/ARTrack.

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Cite this paper

@article{wei2026autoregressive,
  title  = {Autoregressive Visual Tracking},
  author = {Xing Wei and Yifan Bai and Yongchao Zheng and Dahu Shi and Yihong Gong},
  year   = {2026},
  doi    = {10.1109/CVPR52729.2023.00935},
  url    = {https://doi.org/10.1109/CVPR52729.2023.00935},
  journal = {CVPR 2023 2023}
}

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