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Automatic Player Detection and Labeling in Broadcast Soccer Video Using Genetic Algorithm | ||
| Modeling and Simulation in Electrical and Electronics Engineering | ||
| دوره 2، شماره 3 - شماره پیاپی 9، بهمن 2022، صفحه 25-37 اصل مقاله (1.76 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/mseee.2023.27826.1105 | ||
| نویسندگان | ||
| Golafrooz Davoodifar* 1؛ Masoume Gholizade2؛ Mohammad Rahmanimanesh2؛ Rouhollah Haghshenas3؛ Hadi Soltanizadeh2 | ||
| 1Computer Engineering, Islamic Azad Mobarake Branch, Mobarakeh, Isfahan, Iran. | ||
| 2Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. | ||
| 3Faculty of Human Sciences, Semnan University, Semnan, Iran. | ||
| تاریخ دریافت: 25 تیر 1401، تاریخ بازنگری: 09 آبان 1401، تاریخ پذیرش: 01 مهر 1402 | ||
| چکیده | ||
| Due to the increasing amount of video data, a lot of research has been done in the field of retrieving and categorizing this type of data. On the other hand, with the growing popularity of football and the increasing number of its audiences, the importance of automatic and real-time extraction of statistics and information about soccer matches has increased. One of the critical and challenging tasks in soccer video analysis is the detection of players’ blobs and regions, along with identifying the teams related to the players. This task encounters many challenges, including grass loss in the playfield, the presence of playfield lines and players' shadows, the overlapping of players with objects and other players, and different shapes of players in different situations. This paper proposes a framework for detecting players and their related teams. For this purpose, an object-sieve-based method is used to detect players’ blobs, and a genetic algorithm is used to identify their related teams. Each chromosome of the genetic algorithm is a window that lies on one blob whose fitness function shows how much its color and shape characteristics fit with the uniforms of each of the two teams. The proposed method was evaluated by 50 different frames of broadcast soccer videos, including 563 players, and 40 different sub-images, including 84 players. The results show 98% and 91.6% precision for player detection and labeling, respectively. | ||
| کلیدواژه‌ها | ||
| Label detection؛ Soccer video؛ Blob Sieve؛ Genetic Algorithm؛ Video processing | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 240 تعداد دریافت فایل اصل مقاله: 247 |
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