Action Recognition Method in Sports Video Shear Based on Fish Swarm Algorithm


Jie Sun, Lin Lu, Journal of Information Processing Systems Vol. 19, No. 4, pp. 554-562, Aug. 2023  

https://doi.org/10.3745/JIPS.04.0285
Keywords: Action Recognition, Fish Swarm Algorithm, image features, Sports Video, Sports Video Shear
Fulltext:

Abstract

This research offers a sports video action recognition approach based on the fish swarm algorithm in light of the low accuracy of existing sports video action recognition methods. A modified fish swarm algorithm is proposed to construct invariant features and decrease the dimension of features. Based on this algorithm, local features and global features can be classified. The experimental findings on the typical sports action data set demonstrate that the key details of sports action can be successfully retained by the dimensionality-reduced fusion invariant characteristics. According to this research, the average recognition time of the proposed method for walking, running, squatting, sitting, and bending is less than 326 seconds, and the average recognition rate is higher than 94%. This proves that this method can significantly improve the performance and efficiency of online sports video motion recognition.


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Cite this article
[APA Style]
Sun, J. & Lu, L. (2023). Action Recognition Method in Sports Video Shear Based on Fish Swarm Algorithm. Journal of Information Processing Systems, 19(4), 554-562. DOI: 10.3745/JIPS.04.0285.

[IEEE Style]
J. Sun and L. Lu, "Action Recognition Method in Sports Video Shear Based on Fish Swarm Algorithm," Journal of Information Processing Systems, vol. 19, no. 4, pp. 554-562, 2023. DOI: 10.3745/JIPS.04.0285.

[ACM Style]
Jie Sun and Lin Lu. 2023. Action Recognition Method in Sports Video Shear Based on Fish Swarm Algorithm. Journal of Information Processing Systems, 19, 4, (2023), 554-562. DOI: 10.3745/JIPS.04.0285.