A bottom-up approach for activity recognition in smart rooms

We propose a smart camera system where the cameras detect the presence of a person and recognize activities of this person. A relational graph-based modeling of human body and a HMM-based activity recognition of the body parts are proposed for real-time video analysis. The results show that more than 86 percent of the body parts and 88 percent of the activities are correctly classified. We also describe the relationship between the activity detection algorithms and the architectures required to perform these tasks in real time. We achieve a processing rate of more than 20 frames per second for each TriMedia video capture board.