Research On Real-time Visual Tracking Of Moving Objects

With the continuous development of vision technology,the research of real-time visual tracking of moving targets has received widespread attention.The Tracking-Learning-Detection(TLD)algorithm has significant advantages in long-term tracking.When the target disappears for a short time,the TLD algorithm can track the target again after the correction of the learning module.However,in the face of tracking difficulties such as illumination variation,occlusion,background clutters,and out-of-plane rotation,the traditional TLD algorithm is prone to drift and lead to tracking failure,its tracking performance needs to be improved.This paper focuses on the traditional TLD algorithm and its improvements.The main work is as follows:Firstly,this paper studies the target appearance model and similarity measure criteria.A reasonable appearance model can achieve high-efficiency target tracking.The basic principles of color histogram,histogram of oriented gradient,local binary pattern and image pyramid feature are studied.And the target similarity measurement standards and applicable scenarios in image processing are described.Secondly,the principle and tracking process of traditional TLD algorithm are studied in depth.The tracking module of pyramid optical flow method,detection module of multi-cascade classifier,P-N learning module and comprehensive module are studied respectively.The parallel working mechanism of the tracking module and detection module achieves long-term stable tracking of a single target.Then,an improved TLD algorithm based on artificial fish swarm particle filter is proposed.The artificial fish swarm particle filter tracker is used to instead of the optical flow tracker,and it fuse the color histogram feature and histogram of oriented gradient feature to improve the robustness of the target apparent model,introduce the multi-scale ideas of image pyramid to match scale.Through the filtering process of particle filter to predict the target area,the global scanning of TLD algorithm detection module is improvedto local scanning,a large number of non-target areas are eliminated,and the detection efficiency is improved.Based on the visual tracking benchmark video tracking data set,the experimental results show that the improved TLD tracking algorithm based on artificial fish swarm particle filter has good tracking performance.Compared with the traditional TLD algorithm,its average success rate and precision are improved by 19.04% and28.00% respectively,and the average tracking speed can reach 33.87 fps.Finally,the dynamic autonomous visual tracking system is studied.The camera's internal reference matrix is obtained by Zhang Zhengyou's camera calibration method,and the artificial fish swarm particle filter TLD improved algorithm is used to track the target to obtain the pixel coordinates of the target center location.The internal parameter matrix is used to calculate the camera pitch and yaw angle rotations to generate 2 channels of pulse width modulation.The signal drives the steering gear to achieve the purpose of camera autonomous tracking.The experiment is carried out on the platform of Raspberry Pi 3B and camera two-degree-of-freedom gimbal platform.The average errors of the abscissa and ordinate are 8.160 px and 9.519 px respectively,the standard deviations are5.137 px and 6.343 px respectively,and the processing speed can reach 10.25 fps.