Model-based human motion analysis in monocular video

Tracking human motion in monocular video is a challenging problem in computer vision. It has found a wide range of applications, such as visual surveillance, virtual reality, sports science, etc. This project aims to develop a model-based human motion analysis system that can track human movement in a monocular image sequence with minimum constraint. No markers or sensors are attached to the subject. Given a video clip, the first step is to fit the 3D human model manually to the subject in the first frame of the video. Then background subtraction is used to extract the human silhouette. We propose the silhouette chamfer as the main matching feature. A chamfer distance measure is carried out on the extracted subject silhouette. The silhouette chamfer contains both the chamfer distance and region information. Finally, we use a discrete Kalman filter to predict the pose of the subject in each image frame. The updating step uses Broydent's method to optimize the predicted pose to fit the person's silhouette by using the cost function. We use the gait database SOTON to test our system. The image sequences contain human walking in both indoor and outdoor environments. The motion tracking results demonstrate that our system has an encouraging performance.