Research On Pedestrian Detection Technology In Video Sequences

As public safety is more and more widely valued by society,video surveillance technology is used in many important occasions.Among them,pedestrian detection,as a classic target detection type in the field of computer vision,is still a research hotspot of many institutions.In the research process of pedestrian detection,researchers have been plagued by factors such as serious pedestrian occlusion,different pedestrian sizes,and background interference.Many algorithms have been applied to solve various problems in pedestrian detection.This paper is based on different detection algorithms.The research work is as follows:1.In terms of moving object detection,four common detection algorithms are compared and analyzed.According to the shortcomings of the algorithm itself and the complexity of the actual application scenario,a Gaussian mixture model and edge detection algorithm are selected as the main algorithms to filter the Canny edge operator The filter is replaced by a hybrid filter,and the dual-threshold Otsu algorithm is adaptive.The video frame uses the four-frame difference method of inter-frame processing,which avoids interference such as sudden changes in lighting and makes the foreground target contour holes disappear and the boundary is complete.2.In terms of low-level features of traditional artificial design,Haar,LBP,and HOG are used to briefly introduce three image features commonly used for object detection,Adaboost,and SVM classifiers.The feature and classifier combination method is used to detect pedestrians.Different detection effects have obvious data differences in missed detection rate and accuracy rate.The analysis and verification show that HOG is more suitable for pedestrian detection due to its invariance and allows pedestrians to have different attitudes.3.In terms of deep learning,the network structure of the YOLO algorithm is analyzed.In view of the insensitivity of the YOLO algorithm to small target pedestrians,the BN algorithm is added to the neural network structure,and the YOLO network are reconstructed.The target size is not uniform.Using 13×13,26×26,and 52×52 to make up,the detection accuracy of the new YOLO network is improved compared with the previous detection accuracy,and the detection effect of small target pedestrians is improved.4.For pedestrian detection in video,this article uses OpenCV,QT and other frameworks to build a simple pedestrian detection client.It can store and retain local video and real-time video and display the reservation after detection to facilitate detection and comparison.