Tracking algorithm when managing competitive activities of top level teams online based on computer visioncomputer vision
| Autor: | A. A. Polozov, N. A. Maltceva, G. S. Kramarenko, M. A. Lipilin, A. R. Akhmetzyanov |
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| Jazyk: | ruština |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Вестник Дагестанского государственного технического университета: Технические науки, Vol 51, Iss 2, Pp 120-127 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2073-6185 2542-095X |
| DOI: | 10.21822/2073-6185-2024-51-2-120-127 |
| Popis: | Objective. The article presents the results of a study of tracking algorithms for analyzing a basketball game. The purpose of the work is to determine the optimal method for using athlete tracking when used online.Method. The research is based on methods and algorithms for solving management problems in organizational systems.Result. Algorithms with object re-identification are considered, taking into account both motion dynamics and appearance. The most popular tracking algorithms, BYTE, taken from the Bytetrack algorithm, and the Deepsort algorithm, which showed high results in the task of tracking pedestrians in a crowd, were selected as candidates. The algorithms were compared using the MOTA and MOTP tracking assessment quality metrics, as well as the operating time of the algorithms. The experiments were carried out on a general and sports dataset - MOT20 и SportMot.Conclusion. The study showed that the best result in online frame processing is achieved by the ByteTrack algorithm. It showed comparable quality metrics with fast turnaround times. The authors used open implementations of the algorithms and wrote a convenient interface for conducting experiments on different datasets and detection sources. |
| Databáze: | Directory of Open Access Journals |
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