Human Action Recognition

Human Action Recognition Human action recognition is an important topic of computer vision research and applications. The goal of the action recognition is an automated analysis of ongoing events from video data. A reliable system capable of recognizing various human actions has many important applications. The applications include surveillance systems, health-care systems, and a variety of systems that involve interactions between persons and electronic devices such as human-computer interfaces. In this project, the problem of human action recognition from video sequences is addressed. Human Action Recognition(HAR) for both Depth as well as RGB video sequences were analysed. In Depth based HAR, we propose two methods, first with the local features and second uses the global features. In both methods, l1minimization framework was employed for classification. Experiments were performed on Video Analytics Lab(VAL) dataset. For RGB based HAR, Latent Dirichlet Allocation(LDA) was used with Space Time Interest Points(STIP) feature descriptors. STIP effectively captures the local structure in spatio temporal dimensions of the video sequence. Each video sequence was represented as a ’bag-of-visual words’. Experiments were performed on WEIZMANN, KTH and Video Analytics Lab(VAL) databases in two scenarios, one in which number of topics for all actions were constant and manually chosen, another where number of topics for each action changed depending on human action categories.