Detection of articulated objects such as humans is an important task in computer vision. We present a system that incorporates a variety of constraints in a unified multi-view framework to automatically detect humans in possibly crowded scenes. These constraints include the kinematic constraints, the occlusion of one part by another and the high correlation between the appearance of parts such as the two arms. The graphical structure (non-tree) obtained is optimized in a nonparametric belief propagation framework using prior based search.