Silhouette-Based Human Behavior Analysis

• Main Authors: Alan Huang, 黃雅楠
• Other Authors: 廖弘源
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/32491999308904038040

碩士 === 國立交通大學 === 資訊工程系所 === 93 === In this thesis, we develop a real-time visual surveillance system for human behavior analysis. It operates on monocular color-scale video imagery with a stationary background scene. At the first step in the system process, it extracts the silhouette of the target object by traditional video tracking method, background subtraction combined with Gaussian Mixture Model ( GMM ). And furthermore, it detects the contour of the object silhouette. At the second step, the system employs a combination of shape analysis and geometry analysis on the contour to decompose the detected silhouette to several undefined parts（unlabeled body parts）. After the decomposition process, it labels each the separated part （head、torso、hands、feet）by the use of our hierarchical statistical-shape-similarity algorithm ( HSSS ). As the above steps have been processed successfully, the last step in our system is to extract local features of the detected body part（orientation、centroid. . . etc）, and the global features of the entire silhouette（aspect ratio、 block density. . . etc）, and then these features can be used to guide the high-level human behavior analysis. In the on-line behavior analysis process, an unknown sequence will be matched with the templates collected in our database. The database is established offline by the use of real video captures, which is a group of labeled reference sequence representing typical behaviors. In short, our system can detect the human body parts and classify the posture of human at individual imagery, then identify the event of a query sequence which involves human beings. It runs at 20~25Hz for 240 x 160 resolution images on a single Pentium-M 1600Mhz PC.