Human behavior recognition from arbitrary views

This paper presents a new behavior classification system that can analyze human behaviors from arbitrary views. Technically, if different viewing angle are used for observing a person, his appearances will change significantly. To freely recognize his behaviors, traditional methods tend to adopt 3-D data for behavior analysis. However, its inherent correspondence process will make it inappropriate for real time applications. To tackle this problem, a novel view alignment method is first proposed for mapping each action sequence to a fixed view. To achieve this mapping, two features extracted from spatial and temporal domains are used for representing each action sequence. For the spatial feature, the "centroid context" of each posture is defined and extracted through a triangulation technique. For the temporal feature, the "posture transition probability" is constructed for recording the probabilities of one posture type transferring to another one. After mapping, a novel matrix representation is proposed for describing each action more accurately. After that, the Viterbi algorithm is used for aligning two action sequences and then classifying them to different behavior types.