Application of Improved Interactive Multimodel Algorithm in Player Trajectory Feature Matching
The original target tracking algorithm based on a single model has long been unable to meet the complex and changeable characteristics of the target, and then there are problems such as poor tracking accuracy, target loss, and model mismatch. The interactive multimodel algorithm uses multiple motion...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/2993675 |
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| _version_ | 1832568482105917440 |
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| author | Xi Du Qi Ao Lu Qi |
| author_facet | Xi Du Qi Ao Lu Qi |
| author_sort | Xi Du |
| collection | DOAJ |
| description | The original target tracking algorithm based on a single model has long been unable to meet the complex and changeable characteristics of the target, and then there are problems such as poor tracking accuracy, target loss, and model mismatch. The interactive multimodel algorithm uses multiple motion models to track the target, obtains the degree of adaptation between the actual motion state of the target and each model according to the calculated likelihood function, and then combines the updated weight values of each filter to obtain a weighted sum. Therefore, the interactive multimodel algorithm can achieve better performance. This paper proposes an improved interactive multimodel algorithm that can achieve player tracking and trajectory feature matching. First, this paper proposes an improved Kalman filtering (IKF) algorithm. This method is developed from the unbiased conversion measurement Kalman filter, which can obtain more accurate target state and covariance estimation. Secondly, using the parallel processing mode of the IMM algorithm to efficiently solve the data association between multiple filters, the IMM-IKF model is proposed. Finally, in order to solve the problem of low computational efficiency and high mismatch rate in image feature point matching, a method of introducing a minimum spanning tree in two-view matching is proposed. Experimental results show that the improved IMM-IKF algorithm can quickly respond to changes in the target state and can find the matching path with the lowest matching cost. In the case of ensuring the matching accuracy, the real-time performance of image matching is ensured. |
| format | Article |
| id | doaj-art-26ffea9f84a443e0948f5621f9e33f1f |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-26ffea9f84a443e0948f5621f9e33f1f2025-02-03T00:58:59ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/29936752993675Application of Improved Interactive Multimodel Algorithm in Player Trajectory Feature MatchingXi Du0Qi Ao1Lu Qi2Ordos Vocational College, Ordos 017020, Inner Mongolia, ChinaOrdos Vocational College, Ordos 017020, Inner Mongolia, ChinaPeking University, Beijing 100871, ChinaThe original target tracking algorithm based on a single model has long been unable to meet the complex and changeable characteristics of the target, and then there are problems such as poor tracking accuracy, target loss, and model mismatch. The interactive multimodel algorithm uses multiple motion models to track the target, obtains the degree of adaptation between the actual motion state of the target and each model according to the calculated likelihood function, and then combines the updated weight values of each filter to obtain a weighted sum. Therefore, the interactive multimodel algorithm can achieve better performance. This paper proposes an improved interactive multimodel algorithm that can achieve player tracking and trajectory feature matching. First, this paper proposes an improved Kalman filtering (IKF) algorithm. This method is developed from the unbiased conversion measurement Kalman filter, which can obtain more accurate target state and covariance estimation. Secondly, using the parallel processing mode of the IMM algorithm to efficiently solve the data association between multiple filters, the IMM-IKF model is proposed. Finally, in order to solve the problem of low computational efficiency and high mismatch rate in image feature point matching, a method of introducing a minimum spanning tree in two-view matching is proposed. Experimental results show that the improved IMM-IKF algorithm can quickly respond to changes in the target state and can find the matching path with the lowest matching cost. In the case of ensuring the matching accuracy, the real-time performance of image matching is ensured.http://dx.doi.org/10.1155/2021/2993675 |
| spellingShingle | Xi Du Qi Ao Lu Qi Application of Improved Interactive Multimodel Algorithm in Player Trajectory Feature Matching Complexity |
| title | Application of Improved Interactive Multimodel Algorithm in Player Trajectory Feature Matching |
| title_full | Application of Improved Interactive Multimodel Algorithm in Player Trajectory Feature Matching |
| title_fullStr | Application of Improved Interactive Multimodel Algorithm in Player Trajectory Feature Matching |
| title_full_unstemmed | Application of Improved Interactive Multimodel Algorithm in Player Trajectory Feature Matching |
| title_short | Application of Improved Interactive Multimodel Algorithm in Player Trajectory Feature Matching |
| title_sort | application of improved interactive multimodel algorithm in player trajectory feature matching |
| url | http://dx.doi.org/10.1155/2021/2993675 |
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