An IF-RCNN Algorithm for Pedestrian Detection in Pedestrian Tunnels

Abstract

In order to deal with the problems of background mixing, pedestrian blur and pedestrian multi-scale in pedestrian tunnels, we proposed an improved faster region based convolution neural network (IF-RCNN) pedestrian detection method, which uses deep CNN to automatically extract features from pictures instead of traditional manual design features. In this paper, an improved region proposal network (RPN) structure is proposed to solve the multi-scale problem of pedestrians in tunnel. The anchor size in RPN network is further improved in the face of pedestrian images in tunnels with small total pixels. Meanwhile, feature fusion technology is introduced to the algorithm to output the features of different convolution layers. The image is fused to enhance the detection performance of blurred and occluded pedestrians in tunnel. Experimental results show that IF-RCNN algorithm has better detection performance in tunnel data set and VOC2007 data set.

Keywords

• Pedestrian detection
• deep learning
• faster RCNN
• feature fusion