Human action recognition using accumulated motion and gradient of motion from video

This paper presents a method to recognize the action being performed by a human in a video. Applications like video surveillance, highlight extraction and video summarization require the recognition of the activities occurring in the video. The analysis of human activities in video is an area with increasingly important consequences from security and surveillance to entertainment and personal archiving. We propose an action recognition scheme based on accumulated motion and gradient of motion, in which the former is motion based and the latter is appearance based. Firstly, we define an Accumulated Motion Image (AMI) with which energy histograms are built and normalized for extracting features. Then we compute DFT from the energy histograms so that features like mean and variance are obtained. Secondly, we try finding out gradient direction and magnitude by taking a key frame from the video. Again, we extract mean and variance from histogram giving out few more feature vectors. Finally with all the extracted features, we train the system using Dynamic Time Warping(DTW) to recognize the various actions.Public dataset is used for Evaluation.