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Exploring the State-of-the-Art in Multi-Object Tracking: A Comprehensive Survey, Evaluation, Challenges, and Future Directions

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Abstract

Multiple object tracking (MOT), as a typical application scenario of computer vision, has attracted significant attention from both academic and industrial communities. With its rapid development, MOT has becomes an hot topic. However, maintaining robust MOT in complex scenarios still faces significant challenges, such as irregular motion patterns, similar appearances, and frequent occlusions. Based on an extensive investigation into the state-of-the-art MOT, this survey has made the following efforts: 1) listing down preceding MOT approaches and current classifications; 2) surveying the MOT metrics and benchmark databases; 3) evaluating the MOT approaches frequently employed; 4) discussing the main challenges for MOT; and 5) putting forward potential directions for the development of future MOT approaches. By doing so, it strives to provide a systematic and comprehensive overview of existing MOT methods from SDE to TBA perspectives, thereby promoting further research into this emerging and important field.

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Funding

This work was supported in part by the Natural Science Foundation of China under Grant 61671192, and in part by the National Science Foundation for Post-Doctoral Scientists of China under Grant 2017M114, and in part by the Top-Ranking Discipline a Class of Electronics Science and Technology in Zhejiang Province, China.

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  1. College of Big Data and Software Engineering, Zhejiang Wanli University, Ningbo, 315100, China

    Chenjie Du, Ran Jin & Bencheng Chai

  2. School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China

    Chenwei Lin, Yingbiao Yao & Siyu Su

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Du, C., Lin, C., Jin, R. et al. Exploring the State-of-the-Art in Multi-Object Tracking: A Comprehensive Survey, Evaluation, Challenges, and Future Directions. Multimed Tools Appl 83, 73151–73189 (2024). https://doi.org/10.1007/s11042-023-17983-2

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