Research On Moving Object Tracking Algorithm Under Occlusion Scenes

In recent years,the rapid development of computer technology,the popularization of camera equipment,and people's demand for intelligent analysis and processing of video information have promoted the development of the field of computer vision.An important branch in the field of computer vision is video target tracking.Video target tracking can predict the state of the target in the subsequent video image sequence only through the target position,size,color and other information in the first frame of image.The application of video target tracking to people The production and life of the company have brought great convenience.However,in the process of target tracking,some complex scenes are often encountered.These complex scenes refer to scenes with interference factors such as lighting changes,occlusion of the target,deformation,and fast movement.Under the action of these complex scenes,it will cause The tracking effect is poor or even the tracking fails.This paper conducts an in-depth study on the target tracking algorithm based on the correlation filtering theory,and analyzes the interference factors in the video image sequence.Aiming at the problem of target occlusion in the tracking process,two video targets are proposed based on the correlation filtering framework.Tracking algorithm.The main work and innovations of this paper are summarized as follows:(1)Aiming at the problem of tracking failure due to occlusion or error accumulation in traditional correlation filtering algorithms in complex scenes,a multi-feature fusion and anti-occlusion target tracking algorithm is proposed.This method uses related filtering algorithms as the framework,and proposes a variety of feature fusion methods,which enhances the ability to describe features.Secondly,the confidence index is introduced to judge the occlusion state of the target,and the random fern classifier redetecting the target mechanism is proposed.After confirming that the occlusion of the target is lost,the target is located by re-detecting the target.Finally,in the template update stage,the frame difference method is introduced to control the update rate of the model to realize the adaptive update of the template.Experimental results show that the tracking failure of targets under occlusion has been significantly improved under this algorithm,and the tracking performance has been improved.(2)Aiming at the problem of poor tracking robustness and accuracy under the interference of occlusion,deformation,and fast motion during target tracking,an antiocclusion target tracking algorithm based on image data augmentation is proposed.First of all,the method adopts the method of fusion of depth features and related filtering frameworks,and replaces traditional manual features with depth features to improve the ability to describe the target object.Secondly,perform data augmentation processing for the first frame of image to obtain more image samples and provide sufficient feature data for later filter training.Then,the target features extracted by the convolutional network are used to train the filters respectively,and then the three filters are weighted and fused to obtain the target position.In addition,the confidence index and the maximum response value of the response graph are used to determine whether the target is occluded or not.If the target is occluded,the template update is stopped.Experimental results show that through the application of image augmentation and other methods,the algorithm in this paper has been further improved in tracking robustness and accuracy,and can better adapt to complex scenes such as occlusion,rapid motion,and deformation in video sequences.