Abstract

Feature extraction, deformation handling, occlusion handling, and classification are four important components in pedestrian detection. Existing methods learn or design these components either individually or sequentially. The interaction among these components is not yet well ex-plored. This paper proposes that they should be jointly learned in order to maximize their strengths through coop-eration. We formulate these four components into a joint deep learning framework and propose a new deep network architecture1. By establishing automatic, mutual interac-tion among components, the deep model achieves a 9 % re-duction in the average miss rate compared with the cur-rent best-performing pedestrian detection approaches on the largest Caltech benchmark dataset. 1.