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Tracking Small and Fast Moving Objects: A Benchmark

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Computer Vision – ACCV 2022 (ACCV 2022)

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Abstract

With more and more large-scale datasets available for training, visual tracking has made great progress in recent years. However, current research in the field mainly focuses on tracking generic objects. In this paper, we present TSFMO, a benchmark for Tracking Small and Fast Moving Objects. This benchmark aims to encourage research in developing novel and accurate methods for this challenging task particularly. TSFMO consists of 250 sequences with about 50k frames in total. Each frame in these sequences is carefully and manually annotated with a bounding box. To the best of our knowledge, TSFMO is the first benchmark dedicated to tracking small and fast moving objects, especially connected to sports. To understand how existing methods perform and to provide comparison for future research on TSFMO, we extensively evaluate 20 state-of-the-art trackers on the benchmark. The evaluation results exhibit that more effort are required to improve tracking small and fast moving objects. Moreover, to encourage future research, we proposed a novel tracker S-KeepTrack which surpasses all 20 evaluated approaches. By releasing TSFMO, we expect to facilitate future researches and applications of tracking small and fast moving objects. The TSFMO and evaluation results as well as S-KeepTrack are available at https://github.com/CodeOfGithub/S-KeepTrack.

Z. Zhang and F. Wu—These authors contributed equally.

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Authors and Affiliations

  1. Guilin University of Technology, Guilin, 541006, China

    Zhewen Zhang, Fuliang Wu, Yuming Qiu, Jingdong Liang & Shuiwang Li

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  1. Zhewen Zhang
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  2. Fuliang Wu
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  3. Yuming Qiu
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  4. Jingdong Liang
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Correspondence to Shuiwang Li .

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Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Zhang, Z., Wu, F., Qiu, Y., Liang, J., Li, S. (2023). Tracking Small and Fast Moving Objects: A Benchmark. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13847. Springer, Cham. https://doi.org/10.1007/978-3-031-26293-7_33

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