Pedestrian Detection And Tracking Based On ORB Features

With the development of current science and technology,computer vision has become a research hotspot,and more and more applied to all aspects of people's lives.Among them,image stitching technology has broad prospects in practical applications,and can be applied to sports target detection,visual navigation and other fields.And in these fields,many experts and scholars have begun to take pedestrian detection and tracking issues as important research topics.In this paper,the following research is carried out on image stitching and pedestrian detection and tracking:Firstly,the basic theory of image stitching is studied,mainly introducing the flow and algorithm of image registration.Then a matching algorithm based on ORB feature is proposed.The principle of ORB feature is introduced and the simulation results are given.Then introduced the improved image registration process proposed in this paper,mainly the median filter denoising,PROSAC algorithm to eliminate mismatched points and perspective transformation correction.Then the simulation verifies the feasibility of the algorithm and compares it with the SIFT algorithm and the SURF algorithm.Finally,three sets of images with overlapping parts are used for splicing,and an image with large viewing angle and high definition is obtained under cylindrical projection.Then,the pedestrian detection study was continued based on the extracted ORB features.First introduced the common methods of pedestrian detection.There are two classification algorithms used in this article,which are the introduction of the principles of Adaboost and SVM algorithms.Next,using the image samples of the mainstream database on the Internet,the simulation experiments are carried out based on the ORB features combined with the above two classification algorithms.The comparison of the results of the four algorithms shows that:All of them achieve the detection effect on pedestrians,and the algorithms based on ORB features are faster than the HOG-based algorithms.When using the ORB feature,Adaboost's algorithm detection rate is low,but the detection time is short,while the SVM algorithm detection is slow but the correct rate is high,which can achieve accurate detection.Finally,on the basis of pedestrian detection,the technical research of pedestrian tracking was carried out.Firstly,the common algorithms,characteristics and performance evaluation criteria of target tracking technology are introduced.Then I introduce the related theory of the particle filter algorithm of the ORB feature to be used in this paper.The particle filter tracking algorithm combined with the ORB feature needs to be realized by establishing the motion model,detection model and resampling.Next,some video sequences were selected for experimental analysis.The video of multiple people and the pedestrian video with dramatic background changes were selected to simulate the tracking.For the particle filter algorithm,the ORB feature and the HOG feature are used to compare the experiments,and the algorithm based on the ORB feature is faster.Based on the ORB features,the particle filter,MeanShift and Camshift tracking algorithms are used respectively.It is found that Camshift has tracking failure.The accuracy of MeanShift is lower than the particle filtering algorithm in this paper.The experimental results show that although the ORB and particle filter algorithm increases the amount of calculation,for the video with smaller resolution,the tracking performance is better and real-time performance can be realized.After completing the algorithm analysis of the above three contents,the paper designed the system for pedestrian detection and tracking,and carried out system function test and result analysis.