Cricket Shot Analysis using Conditional Directed Spatio-Temporal Graph networks

Recent Use of Conditional Spatio-temporal Directed Graph Convolutional Networks(Cond ST-DGCN) [8] to represent human pose estimation has significantly helped in capturing varying non-local dependencies between limbs for different actions. This can be immensely helpful in Sports analytics where player pose plays key role in shot evaluation and can help in corrective action. In this article, we propose CondDGCN [8] based framework to explore use of spatial-temporal relation of batsman shot sequences (labelled and annotated 2D cricket dataset [1]) for Cricket shot action recognition by conditioning the graph network on batsman 2D poses. We achieve 97% accuracy for shot recognition and further explore visualization of conditional graph connections to establish importance of particular limbs for shots. The proposed framework uses fine-tuned 2D Pose estimator OpenPose [11] (fine-tuned for cricket dataset [1]) which in turn helps in easy adaptation of our solution to internet cricket videos for shot analytics.