Activity recognition through multi-scale motion detail analysis

Activity recognition is one of the most challenging problems in the video content analysis and high-level computer vision. This paper proposes a novel activity recognition approach in which we decompose an activity into multiple interactive stochastic processes, each corresponding to one scale of motion details. For modeling the interactive processes, we present a hierarchical durational-state dynamic Bayesian network (HDS-DBN) to model two stochastic processes which are related to two appropriate scales in intelligent surveillance. In HDS-DBN, states are decomposed in terms of multi-scale motion details, and each kind of state indicates legible meaning. The effectiveness of this approach is demonstrated by experiments of individual activity recognition and two-person interacting activity recognition.