Dressed Human Modeling, Detection and Parts Localization

This dissertation presents an integrated human shape modeling, detection, and body part localization vision system. It demonstrates that the system can (1) detect pedestrians in various shapes, sizes, postures, partial occlusion, and clothing from a moving vehicle us-ing stereo cameras; (2) locate the joints of a person automatically and accurately without employing any markers around the joints. The following contributions distinguish this dissertation from previous work: 1. Dressed human modeling and dynamic model assembling: Unlike previous work that employs a fixed human body model or global deformable template to perform human detection, in this dissertation merged body parts are introduced to represent the defor-mations caused by clothing, segmentation errors, or low image resolution. A dressed human model is dynamically assembled from the model parts in the recognition step; the shapes of the body parts and the size and spatial relationships between them (the contextual information) are represented as invariant under translation, rotation, and scaling. Therefore, the system can detect people in different clothes, positions, sizes