Pedestrian Detection via Body Part Semantic and Contextual Information With DNN

Abstract

Pedestrian detection has achieved great improve-ments in recent years, while complex occlusion handling and high-accurate localization are still the most important problems. To take advantage of the body part semantic information and the contextual information for pedestrian detection, we propose the part and context network (PCN) in this paper. A PCN is composed of three branches: the basic branch; the part branch; and the context branch. It specially utilizes two branches to detect the pedestrians through the body part semantic information and the contextual information, respectively. In the part branch, the semantic information of body parts can communicate with each other via long short-term memory (LSTM). In the context branch, we adopt a local competition mechanism (maxout) 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 higher localization accuracy, especially for the occlusion pedestrian. The combination of the body part semantic information and the contextual information in pedestrian detection is fully explored in this paper. Comprehensive evaluations on three challenging pedestrian detection datasets (i.e., Caltech, INRIA and KITTI) well demonstrate the effectiveness of our proposed PCN. Code for PCN is publicly available on GitHub https://github.com/sunnyxiaohu/pcn_pedestrian.