Discriminative random fields for online behavior recognition

This paper proposes an online behavior recognition based on Discriminative Random Fields. In this model, by incorporating CRF and HCRF, a Frame-HCRF was extended to model behaviors for frames of motion data. The motion intrinsic dynamics are captured by CRF structure as well as extrinsic dynamics between different behaviors by hidden feature functions. This model can accommodate motion data online processing with unknown future frames. The experiments show that the proposed model perform over than HMM, CRF and HCRF for human behavior modeling and recognition.