A study on human activity recognition from video

Analysis of human activities from video is currently one of the ongoing research areas in computer science. Recognition of human activity from video has gained lot of attention because of its increasing demand in many real life applications, for e.g. video surveillance, entertainment, healthcare, child and old age homes, etc. In this paper, several steps involved in automatic human activity recognition systems, such as segmentation, tracking of motion, pose estimation and recognition of activities are studied. Common segmentation techniques such as background subtraction and Gaussian Mixture Model (GMM) are also discussed. Two different approaches for tracking of motion in video, i.e. representation and localization of the target and filtering and data association are also discussed. Finally some of the commonly available datasets used in automatic analysis of human activities from video are also mentioned in detail.