SVM-based State Transition Framework for Dynamical Human Behavior Identification

This investigation proposes an SVM-based state transition framework (named as STSVM) to provide better performance of discriminability for human behavior identification. The STSVM consists of several state support vector machines (SSVM) and a state transition probability model (STPM). The intra-structure information and inter-structure information of a human activity are analyzed and correlated by the SSVM and STPM, respectively. The integration of the SSVM and the STPM effectively provides human behavior understanding. With a database consisting of five kinds of human behaviors: raising hand, standing up, squatting down, falling down, and sitting, the proposed algorithm has been demonstrated with a significant recognition rate of 88.6%.