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AMTSet: a benchmark for abrupt motion tracking

  • 1193: Intelligent Processing of Multimedia Signals
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Abstract

Since the OTB100 benchmark dataset is released, it has been widely used in a large number of researches on object tracking for performance evaluation. However, the existing datasets are insufficient to evaluate trackers in handling different challenging factors. In this paper, we present the first dataset and benchmark for tracking objects with abrupt motion (AMTSet). The dataset consists of 50 videos of special scenes from our real life, such as camera switching, sudden dynamic change, low frame rate video, etc., which are quite challenging in object tracking. Boundary boxes over 10K frames are marked manually, and all of them are manually labelled for common attributes of object tracking, such as scale variation, illumination variation, occlusion, motion blur, etc. We benchmark the dataset on 36 representative trackers and rank them according to the tracking conditions and results. Furthermore, we propose an evaluation measure for object tracking to better highlight the performances of the trackers against abrupt motion. Our goal is to supplement the existing baseline datasets and provide researchers with more perfect baseline data in order to better evaluate the performance of different trackers.

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Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian Minzu University, Dalian, China

    Fasheng Wang, Chang Wang, Shuangshuang Yin, Jianjun He, Fuming Sun & Junxing Zhang

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  1. Fasheng Wang
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  2. Chang Wang
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  3. Shuangshuang Yin
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  4. Jianjun He
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  5. Fuming Sun
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Correspondence to Fuming Sun.

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This work was supported by National Natural Science Foundation of China (No. 61972068, 61976042), Liaoning Baiqianwan Talent Program, Dalian Science Foundation for Young Scholars (No. 2017RQ151), Innovative Talents Project of Liaoning Universities (No. LR2019020).

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Wang, F., Wang, C., Yin, S. et al. AMTSet: a benchmark for abrupt motion tracking. Multimed Tools Appl 81, 4711–4734 (2022). https://doi.org/10.1007/s11042-021-10947-4

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