Deep Spatial/temporal-level feature engineering for tennis-based action recognition - Ning, B.; Na, L. (Ergonomics, 2021)
Tennis has becoming an increasingly popular sport throughout the world. Tennis motion recognition based on 3D video has attracted more and more attention in recent years. The algorithm based on dynamic time warping takes into account the timing sequence information of movements and can solve the uncertainty of human movement at temporal level. By increasing the training samples, the efficiency will decrease accordingly. This work presents a tennis action recognition framework based on action standard sequence. The 3D action video samples are incorporated into action sequences by feature extraction, wherein the action standard sequences are encoded as a sequence averaging optimization problem under the dynamic time normalization metric. The dynamic time normalization barycenter averaging algorithm (DBA) is leveraged to solve this problem. For the tennis scenery with significant differences in the action categories, we study the standard sequence learning of multiple actions, and accordingly propose a DBA-K-means clustering algorithm for unsupervised learning. Herein, a human tennis action recognition by integrating feature optimization and image similarity is proposed. The three dimensional reduction methods, including principal component analysis (PCA),PCA + Pearson, and PCA+ Spearman, were compared to prove that PCA+ Pearson correlation coefficient had the best dimensional reduction effect. Meanwhile, the global feature eight-star model is combined with the local feature HOG feature after dimensionally reduced to fully represent human movements. The similarity between pairwise adjacent frames of images was calculated. The statistical weight of single frame SVM classification results within a discriminant period is adaptively allocated, and finally the body pose recognition results are classified twice. Experiments on standard data set KTH show that the recognition accuracy of this algorithm is 94.5%, which is better than other methods. It has a good application value in the field of video human motion recognition. Also, we have demonstrated that this method can further improve the efficiency and accuracy of action recognition. Effective feature extraction is beneficial to improve the accuracy of subsequent human action recognition.