Tunnel Pedestrian Detection Based on Super Resolution and Convolutional Neural Network

Abstract

Tunnel pedestrian detection is of great significance to traffic safety. However, because the camera used to collect road images in the tunnel video surveillance system is often far away from the ground, the size of the pedestrian target is small. What’s more, in the special monitoring environment of the tunnel, the video image is blurred, the noise is too much, and the resolution is low, which makes the pedestrian target detection difficult. Aiming at the above problems, this paper proposes a new target detection network which cascades super-resolution and target detection networks. Firstly, for the problem that the convolutional neural network is difficult to extract features of low-resolution image, super-resolution is performed before the target detection of the image according to that the super-resolution network can increase the image resolution and enrich the image information. Then, according to the characteristics of the pedestrian target detection task and the relatively fixed size and aspect ratio of pedestrian target in the video image of tunnel, the original RPN network is improved, and a candidate box which is more suitable for the pedestrian target detection task is designed. The experimental results show that the proposed method achieves better detection results in the tunnel pedestrian target detection problem.