Efficient Sparse Representation based Action Recognition in video

摘要

Human Action Recognition (HAR) is an interesting and helpful topic in various real-life applications such as surveillance based security system, computer vision and robotics. The selected features and feature representation methods, classification algorithms decides the accuracy of the HAR systems. A new feature called, Skeletonized STIP (Spatio Temporal Interest Points) is identified and used in this work. The skeletonization on the action video’s foreground frames are performed and the new feature is generated as STIP values of the skeleton frame sequence. Then the feature set is used for initial dictionary construction in sparse coding. The data for action recognition is huge, since the feature set is represented using the sparse representation. To refine the sparse representation the max pooling method is used and the action recognition is performed using SVM classifier. The proposed approach outperforms on the benchmark datasets.