Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box Alignment

Pedestrians in videos have a wide range of appearance factors, such as body poses, occlusions, and complex backgrounds, which make their detection difficult. Moreover, a proposal shift problem causes the loss of body parts, such as head and legs in pedestrian detection, which further degrades the detection accuracy. In this paper, we propose part-level convolutional neural networks (CNNs) for pedestrian detection using saliency and boundary box (BB) alignment. The proposed network consists of two subnetworks: detection and alignment. In the detection subnetwork, we use saliency to remove false positives, such as lamp posts and trees, by combining a fully convolutional network and a class activation map to extract deep features. Subsequently, we adopt the BB alignment on detection proposals in the alignment subnetwork to overcome the proposal shift problem by applying the part-level CNN to recall the lost body parts. The experimental results on various datasets demonstrate that the proposed method remarkably improves accuracy in pedestrian detection and outperforms the existing state-of-the-art techniques.