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LaSOT: A High-quality Large-scale Single Object Tracking Benchmark

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Abstract

Despite great recent advances in visual tracking, its further development, including both algorithm design and evaluation, is limited due to lack of dedicated large-scale benchmarks. To address this problem, we present LaSOT, a high-quality Large-scale Single Object Tracking benchmark. LaSOT contains a diverse selection of 85 object classes, and offers 1550 totaling more than 3.87 million frames. Each video frame is carefully and manually annotated with a bounding box. This makes LaSOT, to our knowledge, the largest densely annotated tracking benchmark. Our goal in releasing LaSOT is to provide a dedicated high quality platform for both training and evaluation of trackers. The average video length of LaSOT is around 2500 frames, where each video contains various challenge factors that exist in real world video footage,such as the targets disappearing and re-appearing. These longer video lengths allow for the assessment of long-term trackers. To take advantage of the close connection between visual appearance and natural language, we provide language specification for each video in LaSOT. We believe such additions will allow for future research to use linguistic features to improve tracking. Two protocols, full-overlap and one-shot, are designated for flexible assessment of trackers. We extensively evaluate 48 baseline trackers on LaSOT with in-depth analysis, and results reveal that there still exists significant room for improvement. The complete benchmark, tracking results as well as analysis are available at http://vision.cs.stonybrook.edu/~lasot/.

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Notes

  1. Note that for tracking benchmark using full overlap split protocol, category bias should be inhibited in both training and evaluation of trackers. For tracking benchmark using one-shot split protocol, category bias should be inhibited in only training of trackers.

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Acknowledgements

We thank the anonymous reviewers for insightful suggestions, and Jeremy Chu for proofreading the final draft. Ling was supported partially by the Amazon AWS Machine Learning Research Award.

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  1. H. Fan and H. Bai make equal contribution to this work.

Authors and Affiliations

  1. Stony Brook University, Stony Brook, USA

    Heng Fan,  Harshit, Mingzhen Huang & Haibin Ling

  2. Temple University, Philadelphia, USA

    Hexin Bai, Fan Yang, Peng Chu, Ge Deng, Sijia Yu & Juehuan Liu

  3. South China University of Technology, Guangzhou, China

    Liting Lin & Yong Xu

  4. Peng Cheng Laboratory, Shenzhen, China

    Liting Lin & Yong Xu

  5. HiScene Information Technologies, Shanghai, China

    Chunyuan Liao

  6. Amazon Web Services, Palo Alto, USA

    Lin Yuan

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  1. Heng Fan
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Fan, H., Bai, H., Lin, L. et al. LaSOT: A High-quality Large-scale Single Object Tracking Benchmark. Int J Comput Vis 129, 439–461 (2021). https://doi.org/10.1007/s11263-020-01387-y

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