Abstract
An IF-RCNN Algorithm for Pedestrian Detection in Pedestrian Tunnels
Highlights
Pedestrian detection is widely used in intelligent video monitoring, vehicle auxiliary automatic driving, target detection and other fields [1]
This paper focuses on pedestrian detection in pedestrian tunnels, so it pays special attention to the results of improved faster region based convolution neural network (IF-RCNN) algorithm on VOC 2007 human target data
The results show that the map of IF-RCNN in coco data can reach 38.8%, which is 3.9% higher than the original fast RCNN and 0.6% higher than the mask RCNN
Summary
Pedestrian detection is widely used in intelligent video monitoring, vehicle auxiliary automatic driving, target detection and other fields [1]. It is a challenging problem in computer vision. Pedestrian tunnels have the characteristics of complex environment, dim light and large noise interference. The pedestrians monitored in video images have the problems of small size, low resolution, scale change and overlap of pedestrians. Because of its special environment, the tunnel image contains the common problems of target distortion, multi-scale, occlusion, illumination and so on in the problem of target detection and pedestrian detection. Effective perception and monitoring of pedestrian in tunnel are of
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