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

Abstract

Basketball is a sport with a wide range of complicated human movements, and the ability to accurately identify these movements is critical in both competition and training. Training athletes is largely based on the subjective observations and experiences of the coaches. Artificial intelligence and big data technologies can be used to monitor athlete training. It can also assist coaches in making decisions which significantly increase the athletic ability by detecting their movements. This research work proposed a mechanism called BSTARNet which is an action recognition method for basketball sports training. The method is based on artificial neural network (ANN), and network is trained through basketball sports big data. First, this work uses the Convolution Long Short-Term Memory (ConvLSTM) unit to extract the spatiotemporal information features of basketball sports training from videos. Second, this work establishes the Attention Long Short-Term Memory (AttLSTM) unit that combines the attention mechanism with the LSTM. The unit selectively scans each location, giving more attention to the area where the action takes place. Finally, the network framework is built by improving the ordinary encoder-decoder model. After that, the spatiotemporal information contained in the video is encoded based on the Darknet network model. In the decoding stage, the AttLSTM structure is used to replace the ordinary LSTM. These units are combined to form the BSTARNet architecture. Experiments are conducted to verify the effectiveness of the proposed method applied on action recognition in basketball sports training and achieved 89.5% mAP and 95.4% accuracy.

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

“…Our experimental results conclude that back propagation (BP) is the most effective of the four ANN algorithms regarding action recognition. The average accuracy rate for detecting upper limb movements among basketball players in training is nearly 93.3%, with an immediate recall average of close to 93.3% [26]. The average accuracy rate for detecting lower limb movements among basketball players in training is close to 99.4%, with an immediate recall average of 99.4%.…”

“…As part of his dissertation, Si Zhong (2022) created a system called BSTARNet, an action recognition approach to basketball sports instruction. Artificial neural networks (ANN) form the basis of the process, with the network being trained using data from the world of professional basketball [25]. The Convolution Long Short-Term Memory (ConvLSTM) unit extracts spatiotemporal information aspects of basketball sports training from videos.…”