Pedestrian passage and height detection system based on deep learning

Object detection is an important research direction in the field of machine vision, which is widely used in industry, security and intelligent transportation. In the intelligent traffic gate detection, most of the visual detection is concentrated on face detection, which can not detect the behavior of people who follow or walk side by side and their height. In order to solve this problem, we proposed a visual detection algorithm based on Yolo V3 deep learning network model, and trained the network model for pedestrian detection; The tracking algorithm based on Yolo V3 sort adopts two-line detection, which can not only detect the following, side-by-side and other abnormal behaviors, but also effectively solve the problem of single line detection; At the same time, the target pedestrian is segmented by using Yolo V3 grabcut, and the segmented image is filtered and binarized. After traversing the pixels, the height detection is realized based on the optical axis aggregation model. The experimental results show that the improved network model realizes the detection of ticket evasion behavior such as tailing, side by side and height detection, improves the accuracy of pedestrian detection, and can meet the needs of intelligent transportation field.