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Title: Model-based human motion analysis in single view video
Other Titles: Li yong ren ti mo xing dui dan jiao du ying xiang zuo dong tai fen xi
Authors: Lok, Wai Wong (駱偉煌)
Department: Dept. of Computer Engineering and Information Technology
Degree: Master of Philosophy
Issue Date: 2004
Publisher: City University of Hong Kong
Subjects: Computer vision
Human locomotion -- Computer simulation
Image processing -- Digital techniques
Three-dimensional imaging
Notes: CityU Call Number: TA1637.L64 2004
Includes bibliographical references (leaves 103-108)
Thesis (M.Phil.)--City University of Hong Kong, 2004
ix, 108 leaves : ill. (some col.) ; 30 cm.
Type: Thesis
Abstract: 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 monocular image sequence with minimum constraint. No markers or sensors are attached to the subject. There is no need for the subject to wear tight clothing and occlusion will not seriously affect the tracking process. Monocular 3D human motion tracking is challenging, because the system needs to estimate a large number of degrees of freedom (DOFs) given the minimal amount of motion information. In this project, the number of DOFs is reduced to 12 under the assumption that the subject is walking parallel to the image plane. Given a clip of the video, the first step is to manually fit the 3D human model to the subject in the first frame of the video. Then background subtraction is used to extract the human silhouette. The background modeling and subtraction algorithm uses both color and edge information. A confidence map is used to fuse intermediate results and represent the results of background subtraction. Therefore, we propose the silhouette chamfer as the main matching feature. Chamfer distance measure is carried out on the extracted subject silhouette. The silhouette chamfer contains both the chamfer distance and region information. The chamfer algorithm searches for the best fit of edge points from two different images. Finally, we use discrete Kalman filter to predict the pose of the subject in each image frame. The update step uses Broydent’s method to optimize the predicted human figure’s parameters to fit the person’s silhouette by using the cost function. We use the gait database SOTON from the University of Southampton, UK to test our system. The testing image sequences contain human walking in both the indoor and outdoor environment. The motion tracking results demonstrate that our system has an encouraging performance.
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