Pedestrian detection method based on Faster R-CNN

Pedestrian detection based on computer vision is an important branch of object recognition, which is applied to intelligent monitoring, intelligent driving, robot and so on. At present, many pedestrian detection methods are proposed. However, because of the complexity of the background, pedestrian posture diversity and pedestrian occlusions, pedestrian detection is still a challenge which calls for precise algorithms. In this paper, the fast Region-based Convolutional Neural Network (Faster R-CNN) is used. Firstly, image features were extracted by CNN. After that, we built up a Region Proposal Network to extract regions that might contain pedestrians combined with K-means cluster analysis. And the region is identified and classified by detection network. Finally, the method was tested in the INRIA data set. The results show that the method of pedestrian detection based on Faster R-CNN, which achieves the accuracy of 92.7%, performs better, compared with other algorithms.