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People tracking accuracy improvement in video by matching relevant trackers and YOLO family detectors
H. Quan 1, G. Ma 2, Y. Weichen 2, R. Bohush 3, F. Zuo 4, S. Ablameyko 1,5

1 Belarusian State University, 220030, Minsk, Republic of Belarus, Nezavisimosti av. 4;
2 EarthView Image Inc., 313200, China Deqing County, Zhejiang, Keyuan Road 11;
3 Polotsk State University, 211440, Novopolotsk, Republic of Belarus, Blokhin str. 29;
4 Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks,
Henan University, 450046, China, Kaifeng;
5 United Institute of Informatics Problems, National Academy of Sciences of Belarus,
220012, Minsk, Republic of Belarus, Surganov str. 6

 PDF, 785 kB

DOI: 10.18287/2412-6179-CO-1422

Pages: 734-744.

Full text of article: English language.

Abstract:
The tracking-by-detection paradigm is widely used for people multi-object tracking tasks. Up to now, there exist many detectors and trackers, many evaluation benchmarks, which necessitates the use of relatively uniform estimation methods and metrics. It leads to necessity to choose better combined models of detectors and trackers. To solve this task, we developed a comprehensive performance evaluation methodology for estimation of people tracking accuracy and real-time by using different detectors and trackers. We conducted experiments by choosing the official pre-trained models of YOLOv5, YOLOv6, YOLOv7, YOLOv8 with representative BoTSORT, ByteTrack, DeepOCSORT, OCSORT, StrongSORT trackers under two benchmarks of MOT17 and MOT20. Detailed metrics in terms of error and speed such as higher order tracking accuracy and frames per second were analyzed for the combinations of detectors and trackers. It is concluded that the OCSORT+YOLOv6l model has the best comprehensive performance and the combination of OCSORT and YOLOv7 has the best average performance under MOT17 and MOT20.

Keywords:
YOLO family detectors, tracking-by-detection, multi-object tracking, scoring function, comprehensive performance, video surveillance.

Citation:
Quan H, Ma G, Weichen Y, Bohush R, Zuo F, Ablameyko S. People tracking accuracy improvement in video by matching relevant trackers and YOLO family detectors. Computer Optics 2024; 48(5): 734-744. DOI: 10.18287/2412-6179-CO-1422.

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