Bottom-up Pose Invariant Human Detection with Mutually Compatible Body Part Configuration

• Main Authors: Wang, Yao-Sheng, 王耀笙
• Other Authors: Wang, Sheng-Jyh
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/71791496229021363803

碩士 === 國立交通大學 === 電子工程學系 電子研究所 === 102 === In this thesis, we focus on the detection of human with arbitrary poses in different view-points in static images. To handle this issue, recently representative works need to produce lots of detectors to cover the cases of human with arbitrary poses in different view-points. In this way, the computation cost will be increased exponentially. To prevent this dilemma, we restrict body parts for detection to be limb, head, face or torso, which have high probability to be observed in arbitrary poses and view-points. Compared to related works in the literature, several different opinions are proposed. Firstly, a patch based approach is proposed to model the limb instead of parallel lines or well-segmented half limb used in related works. Secondly, a strong classifier with the “Deformable Part Model” proposed by Felzenszwalb et al. [1] is adopted to cover more variation on head-torso shape, instead of using the rectangular shape assumption for torso. Thirdly, we consider configuration inference as a label assignment problem, instead of a model fitting problem, in order to handle the limitation caused by occlusion or missing parts. Finally, instead of exhaustive search, segmentation information and native property of limb are adopted to reduce the searching space.