Journal
SIGNAL IMAGE AND VIDEO PROCESSING
Volume 15, Issue 7, Pages 1593-1600Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s11760-021-01893-7
Keywords
Table tennis; Sports action recognition; Stroke-type recognition; Deep neural network; Multilabel classification
Funding
- Estonian Centre of Excellence in IT (EXCITE) - European Regional Development Fund
- NVIDIA Corporation
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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.
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.
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