Study of human action recognition based on improved spatio-temporal features

Most of the existed action recognition methods mainly utilise spatio-temporal descriptors of single interest point ignoring their potential integral information, such as spatial distribution information. By combining local spatio-temporal feature and global positional distribution information (PDI) of interest points, a novel motion descriptor is proposed in this chapter. The proposed method detects interest points by using an improved interest points detection method. Then 3-dimensional scale-invariant feature transform (3D SIFT) descriptors are extracted for every interest point. In order to obtain compact description and efficient computation, Principal Component Analysis (PCA) method is utilised twice on the 3D SIFT descriptors of single-frame and multi-frame. Simultaneously, the PDI of the interest points are computed and combined with the above features. The combined features are quantified and selected and finally tested by using Support Vector Machine (SVM) and AdaBoost-SVM recognition algorithm on the public KTH dataset. The testing results showed that the recognition rate has been significantly improved. Meantime, the test results verified the proposed features can more accurately describe human motion with high adaptability to scenarios.