Machine Learning-Based Multitarget Tracking of Motion in Sports Video
In this paper, we track the motion of multiple targets in sports videos by a machine learning algorithm and study its tracking technique in depth. In terms of moving target detection, the traditional detection algorithms are analysed theoretically as well as implemented algorithmically, based on whi...
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5533884 |
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| _version_ | 1832547626116972544 |
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| author | Xueliang Zhang Fu-Qiang Yang |
| author_facet | Xueliang Zhang Fu-Qiang Yang |
| author_sort | Xueliang Zhang |
| collection | DOAJ |
| description | In this paper, we track the motion of multiple targets in sports videos by a machine learning algorithm and study its tracking technique in depth. In terms of moving target detection, the traditional detection algorithms are analysed theoretically as well as implemented algorithmically, based on which a fusion algorithm of four interframe difference method and background averaging method is proposed for the shortcomings of interframe difference method and background difference method. The fusion algorithm uses the learning rate to update the background in real time and combines morphological processing to correct the foreground, which can effectively cope with the slow change of the background. According to the requirements of real time, accuracy, and occupying less video memory space in intelligent video surveillance systems, this paper improves the streamlined version of the algorithm. The experimental results show that the improved multitarget tracking algorithm effectively improves the Kalman filter-based algorithm to meet the real-time and accuracy requirements in intelligent video surveillance scenarios. |
| format | Article |
| id | doaj-art-35613496c5ba4925b24bc88c30d7ca62 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-35613496c5ba4925b24bc88c30d7ca622025-02-03T06:43:57ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55338845533884Machine Learning-Based Multitarget Tracking of Motion in Sports VideoXueliang Zhang0Fu-Qiang Yang1Department of PE and Art Education, Zhejiang Yuexiu University, Shaoxing, Zhejiang 312000, ChinaSchool of Data and Computer Science, Shandong Women’s University, Jinan, Shandong 250002, ChinaIn this paper, we track the motion of multiple targets in sports videos by a machine learning algorithm and study its tracking technique in depth. In terms of moving target detection, the traditional detection algorithms are analysed theoretically as well as implemented algorithmically, based on which a fusion algorithm of four interframe difference method and background averaging method is proposed for the shortcomings of interframe difference method and background difference method. The fusion algorithm uses the learning rate to update the background in real time and combines morphological processing to correct the foreground, which can effectively cope with the slow change of the background. According to the requirements of real time, accuracy, and occupying less video memory space in intelligent video surveillance systems, this paper improves the streamlined version of the algorithm. The experimental results show that the improved multitarget tracking algorithm effectively improves the Kalman filter-based algorithm to meet the real-time and accuracy requirements in intelligent video surveillance scenarios.http://dx.doi.org/10.1155/2021/5533884 |
| spellingShingle | Xueliang Zhang Fu-Qiang Yang Machine Learning-Based Multitarget Tracking of Motion in Sports Video Complexity |
| title | Machine Learning-Based Multitarget Tracking of Motion in Sports Video |
| title_full | Machine Learning-Based Multitarget Tracking of Motion in Sports Video |
| title_fullStr | Machine Learning-Based Multitarget Tracking of Motion in Sports Video |
| title_full_unstemmed | Machine Learning-Based Multitarget Tracking of Motion in Sports Video |
| title_short | Machine Learning-Based Multitarget Tracking of Motion in Sports Video |
| title_sort | machine learning based multitarget tracking of motion in sports video |
| url | http://dx.doi.org/10.1155/2021/5533884 |
| work_keys_str_mv | AT xueliangzhang machinelearningbasedmultitargettrackingofmotioninsportsvideo AT fuqiangyang machinelearningbasedmultitargettrackingofmotioninsportsvideo |