Abstract

Recent development in depth sensors opens up new challenging task in the field of computer vision research areas, including human-computer interaction, computer games and surveillance systems. This paper addresses shape and motion features approach to observe, track and recognize human silhouettes using a sequence of RGB-D images. Under our proposed activity recognition framework, the required procedure includes: detecting human silhouettes from the image sequence, we remove noisy effects from background and track human silhouettes using temporal continuity constraints of human motion information for each activity, extracting the shape and motion features to identify richer motion information and then these features are clustered and fed into Hidden Markov Model (HMM) to train, model and recognize human activities based on transition and emission probabilities values. During experimental results, we demonstrate this approach on two challenging depth video datasets: one based on our own annotated database and other based on public database (i.e., MSRAction3D). Our approach shows significant recognition results over the state of the art algorithms.