Solving Occlusion Problem in Pedestrian Detection by Constructing Discriminative Part Layers

Abstract

Occlusion handling is one of the most challenging issues for pedestrian detection, and no satisfactory achievement has been found in this issue yet. Using human body parts has been considered as a reasonable way to overcome such an issue. In this paper, we propose a brand new approach based on the fusion of Mid-level body part mining and Convolutional Neural Network (CNN) to solve this problem, named DP-CNN(Discriminative Parts CNN). Two main discussions are included in this paper. First, we take an exhaustive analysis on how to mine useful body parts that contribute to pedestrian detection. Multiple ingredients (e.g. feature representation, pedestrian attributes) are analyzed through a wide range of experiments. Second, we convert the part detectors to the middle layer of CNN and re-train the model to get a better adaption of the dataset. Compare to existing approaches based on fine-tuning CNN models, our method is not only robust to occlusion handling but also has a smaller computational cost.