Pedestrian Motion Path Detection Method Based on Deep Learning and Foreground Detection

Meiman Li,Wenfu Xie,Wei Wang

doi:10.1155/2021/5596135

Abstract

For the surveillance video images captured by monocular camera, this paper proposes a method combining foreground detection and deep learning to detect moving pedestrians, making full use of the invariable background of video image. Firstly, the motion region is extracted by the method of interframe difference and background difference. Then, the normalized motion region extracts the feature vectors based on the improved YOLOv3 tiny network. Finally, the trained linear support vector machine is used for pedestrian detection, and the performance of the fusion detection algorithm on caviar dataset is given, which proves the effectiveness of the proposed fusion detection algorithm. Experimental results show that the proposed method not only improves the practical application of pedestrian rerecognition but also reduces the detection range, computational complexity, and false detection rate compared with sliding window method.

Highlights

Pedestrian detection is an important research problem in computer vision
(2) As shown in equations (2) whether the center point of the pedestrian detection boundary box (x, y, w, h) based on improved YOLOv3 tiny network is in a certain height range of the moving object detection boundary box based on background difference method is judged, which is determined by μ
Most of the research focuses on the optimization of deep convolution neural network and its associated technology. e fusion detection algorithm proposed in this paper provides a new idea to improve the two indicators at the same time. e moving target detection algorithm based on background difference method is used to select and modify the detection results of pedestrian detection algorithm based on improved YOLOv3 tiny network under low confidence threshold. e fusion detection algorithm does not rely on artificial confidence threshold and can effectively improve the detection accuracy and recall rate at the same time

Summary

Introduction

Pedestrian detection is an important research problem in computer vision. In recent years, with the development of machine learning, pedestrian detection has made great progress. e main content of pedestrian detection is to detect and locate pedestrians quickly and accurately in the image. is technology has a wide range of applications in the field of car driving assistance, human-computer interaction, and machine vision [1]. E pedestrian detection algorithm based on improved YOLOv3 tiny network can adjust the accuracy and recall rate by setting different confidence thresholds. E fusion algorithm proposed in this paper combines the detection boundary frames obtained by the above two detection algorithms and uses deep learning method to describe the appearance of pedestrian object accurately and comprehensively, while mining the motion information of the pedestrian object [15,16,17,18]. E process of fusion detection algorithm is as follows: firstly, the parameters of pedestrian detection model based on the improved YOLOv3 tiny network are trained by using the dataset, and a frame only containing background in the video is taken as the background.

Result output

Design of Pedestrian Movement Path Detection System

Findings

Experiment and Analysis