  Pedestrians in videos have a wide range of appearances such as body poses,
occlusions, and complex backgrounds, and there exists the proposal shift
problem in pedestrian detection that causes the loss of body parts such as head
and legs. To address it, we propose part-level convolutional neural networks
(CNN) for pedestrian detection using saliency and boundary box alignment in
this paper. The proposed network consists of two sub-networks: detection and
alignment. We use saliency in the detection sub-network to remove false
positives such as lamp posts and trees. We adopt bounding box alignment on
detection proposals in the alignment sub-network to address the proposal shift
problem. First, we combine FCN and CAM to extract deep features for pedestrian
detection. Then, we perform part-level CNN to recall the lost body parts.
Experimental results on various datasets demonstrate that the proposed method
remarkably improves accuracy in pedestrian detection and outperforms existing
state-of-the-arts in terms of log average miss rate at false position per image
(FPPI).
