Authors:
Boris Bačić
1
and
Ishara Bandara
2
Affiliations:
1
Auckland University of Technology, Auckland, New Zealand
;
2
Robert Gordon University, Aberdeen, U.K.
Keyword(s):
Computer Vision, Deep Learning, Spatiotemporal Data Classification, Human Motion Modelling and Analysis (HMMA), Sport Science, Augmented Broadcasting.
Abstract:
In this paper, we contribute to the existing body of knowledge of video indexing technology by presenting a novel approach for recognition of tennis strokes from consumer-grade video cameras. To classify four categories with three strokes of interest (forehand, backhand, serve, no-stroke), we extract features as a time series from stick figure overlays generated using OpenPose library. To process spatiotemporal feature space, we experimented with three variations of LSTM-based classifier models. From a selection of publicly available videos, trained models achieved an average accuracy of between 97%–100%. To demonstrate transferability of our approach, future work will include other individual and team sports, while maintaining focus on feature extraction techniques with minimal reliance on domain expertise.