The Application of Artificial Intelligence and Big Data Technology in Basketball Sports Training

INTRODUCTION: Basketball involves a wide variety of complex human motions. Thus, recognizing them with Precision is essential for both training and competition. The subjective perceptions and experiences of the trainers are heavily relied upon while training players. Big data and Artificial Intelligence (AI) technology may be utilized to track athlete training. Sensing their motions may also help instructors make choices that dramatically improve athletic ability. OBJECTIVES: This research paper developed an Action Recognition technique for teaching basketball players using Big Data, and CapsNet called ARBIGNet METHODS: The technique uses a network that is trained using large amounts of data from basketball games called a Whale Optimized Artificial Neural Network (WO-ANN) which is collected using capsules. In order to determine the spatiotemporal information aspects of basketball sports training from videos, this study first employs the Convolution Random Forest (ConvRF) unit. The second accomplishment of this study is creating the Attention Random Forest (AttRF) unit, which combines the RF with the attention mechanism. The study used big data analytics for fast data transmissions. The unit scans each site randomly, focusing more on the region where the activity occurs. The network architecture is then created by enhancing the standard encoder-decoder paradigm. Then, using the Enhanced Darknet network model, the spatiotemporal data in the video is encoded. The AttRF structure is replaced by the standard RF at the decoding step. The ARBIGNet architecture is created by combining these components. RESULTS: The efficiency of the suggested strategy implemented on action recognition in basketball sports training has been tested via experiments, which have yielded 95.5% mAP and 98.8% accuracy.