2-D structure-based gait recognition in video using incremental GMM-HMM

Gait analysis is a feasible approach for human identification in intelligent video surveillance. However, the effectiveness of the dominant silhouette-based approaches are severely affected by dressing, bag, hair style and the like. In this paper, we propose a useful 2-D structural feature, named skeleton-based feature, effective improvements for human pose estimation in human walking environment and a recognition framework based on GMM-HMM using incremental learning, which can greatly improve the availability of gait traits in intelligent video surveillance. Our skeleton-based feature uses a 15-DOFs, which is effective in eliminating the interference of dressing, bag, hair style and the like, to represent the torso. In addition, to imitate the natural way of human walking, a Hidden Markov Model (HMM) representing the gait dynamics of human walking incrementally evolves from an average human walking model that represents the average motion process of human walking. Our work makes the gait recognition more robust to noise. Experiments on widely adopted databases prove that our proposed method achieves excellent performance.