Model-based human motion tracking and behavior recognition using hierarchical finite state automata

Abstract. The generation of motion of an articulated body for computer animation is an expensive and time-consuming task. Recognition of human actions and interactions is important to video annotation, automated surveillance, and content-based video retrieval. This paper presents a new model-based human-intervention-free approach to articulated body motion tracking and recognition of human interaction using static-background monocular video sequences. This paper presents two major applications based on basic motion tracking: motion capture and human behavior recognition. To determine a human body configuration in a scene, a 3D human body model is postulated and projected on a 2D projection plane to overlap with the foreground image silhouette. We convert the human model body overlapping problem into a parameter optimization problem to avoid the kinematic singularity problem. Unlike other methods, our body tracking does not need any user intervention. A cost function is used to estimat