Abstract
This thesis will discuss the vision-based human motion analysis. The research field contains the model-based hand and walking human body motion analysis, and the appearance-based human gait recognition. A 3-D hand model is developed to track the rotating hand with finger movements in multi-view. To solve the difficulty of high dimensionality, a new modified version of particle filter - separable state based particle filtering (SSBPF) is proposed. Then, by integrating different features in different view angles, the proposed motion tracking system can capture the hand motion parameter effectively and solve the self-occlusion problem of the finger motion.We also propose a system to track the walking body parameters in the videos from different perspectives. A 3D human model with structural and kinematic constraints is constructed. The shape and color histogram of the image is obtained as the observation. Our system improves the traditional annealed particle filter (APF) with the pre-trained joint angle spatial correlation and the temporal constraint, especially when self-occlusion occurs.A gait analysis method is also developed to extract the dynamic and static information from the input video for walking path determination and human identification. The extracted dynamic and static feature is then transformed into lower dimensional embedding space. A hybrid human ID recognition based on the walking velocity of human object is proposed, which can select the more effective feature. Given a test feature vector, the nearest neighbor classifier is applied for walking paths determination and human identification. The proposed algorithm is evaluated on the CASIA gait database, and the experimental results demonstrate a highly acceptable recognition rate.