Study about Football Action Recognition Method Based on Deep Learning and Improved Dynamic Time Warping Algorithm

Abstract

Artificial intelligence recognition of human actions has been used in various fields. This article is based on deep learning and improved dynamic time regularization algorithms to study football action postures. This paper proposes a hierarchical recurrent network for understanding team sports activities in image and position sequences. In the hierarchical model, this article integrates the proposed multiple human-centered features on the time series based on LSTM output. In order to realize this scheme, the holding state is introduced as one of the external controllable states in LSTM, and the hierarchical LSTM is extended to include the integration mechanism. Test outcomes demonstrate those adequacies of the recommended framework, which includes progressive LSTM human-centred benefits. In this study, the improvement of the reference model in the two-stream LSTM-based method is shown. Specifically, by combining human-centered features and meta-information (e.g., location data) into the postfusion framework proposed in the article, the article also proves that the action categories have increased, and the observations enhanced the robustness of fluctuations in the number of football players. The experimental data shows that 67.89% of the postures of football players through this algorithm can be recognized by the improved dynamic time warping algorithm.