Abstract:
"
In this research, a novel approach for stroke classification in sports using video based
technologies is introduced. With the proposed approach strokes can be identified and
classified in near real time from streaming videos. The approach uses a windowing approach
to identify stroke play events and then uses human motion modeling and analysis (HMMA)
techniques to extract spatio-temporal features from videos. The spatio-temporal time series
datasets are used with deep neural networks (LSTM variants) for classification of strokes.
Proposed approach can be used with multiple sports like cricket, tennis, badminton, table
tennis, baseball etc. In this thesis, implementation of the proposed approach with two
different sports (cricket and tennis) is discussed.
Three LSTM variants (LSTM network, Bi-directional LSTM network, CNN- LSTM
network) have been tested in order to find the most suitable neural network. All trained
classifiers in both case studies achieved over 95% average accuracies with the proposed
approach. Bi-directional LSTM network has achieved the best accuracy for the classification
task in both case studies. The proposed approach has the potential to be useful in areas like
augmented coaching, television broadcasting, sports analytics etc. Future enhancements to
this proposed approach will include use of the proposed approach for augmented coaching
with mobile technologies."
