  Pedestrian detection has achieved great improvements in recent years, while
complex occlusion handling is still one of the most important problems. To take
advantage of the body parts and context information for pedestrian detection,
we propose the part and context network (PCN) in this work. PCN specially
utilizes two branches which detect the pedestrians through body parts semantic
and context information, respectively. In the Part Branch, the semantic
information of body parts can communicate with each other via recurrent neural
networks. In the Context Branch, we adopt a local competition mechanism for
adaptive context scale selection. By combining the outputs of all branches, we
develop a strong complementary pedestrian detector with a lower miss rate and
better localization accuracy, especially for occlusion pedestrian.
Comprehensive evaluations on two challenging pedestrian detection datasets
(i.e. Caltech and INRIA) well demonstrated the effectiveness of the proposed
PCN.
