Fast Neural Accumulator (NAC) Based Badminton Video Action Classification

Automatic understanding of sports is essential to improve viewer experience and for coaches and players to analyze, strategize and improve game performance. To achieve this it is essential to harness the ability to localize and recognize the actions in sports videos. In this paper we focus on the fast paced sport of badminton. The challenge is to extract relevant spatio-temporal features from several consecutive frames and to classify them as an action or a no-action in minimal time and with minimal computational power. We propose two novel Neural Accumulator (NAC) based frameworks, namely NAC-LSTM and NAC-Dense for aforementioned objective. Neural Accumulator is employed for spatial and temporal feature extraction respectively followed by classification. The actions of the players were annotated as react,lob, forehand, smash,backhand and serve. An Autoencoder-LSTM Network, Dilated Temporal Convolutional Networks (TCN) and Long Term Recurrent Convolutional Network (LRCN) have been designed for comparison. Multiclass recognition has been performed with 5-fold cross validation on several test-train data splits (from 10–50%) to verify the efficacy of the results. The proposed methods achieve a high classification accuracy in strikingly minimal CPU time.