Spatiotemporal based table tennis stroke-type assessment

This paper presents a proposed multistage deep neural network pipeline for sports action recognition. The proposed pipeline is based on the classification of stroke types of table tennis using spatiotemporal features. The proposed network predicts the final class with different aspects of the final class at each stage. Outcomes of each stage are then fused together to obtain the final prediction. We utilize four different methods that are used in each stage, namely RGB image-based, optical flow-based, pose-based, and region-of-interest-based methods. We conducted our experiments on the TTSTROKE-21 dataset, which has been introduced in MediaEval Challenge 2020. Experimental results show that our proposed methodology obtains 90.7% test accuracy using a combination of RGB images and optical flow-based methods together.

Automated summary

This paper introduces a proposed multistage deep neural network pipeline for sports action recognition, focusing on table tennis stroke types. Experimental results demonstrate that our proposed methodology achieves 90.7% test accuracy on the TTSTROKE-21 dataset by combining RGB images and optical flow-based methods.