Application Research On Target Tracking Based On Constrained Local Model Matching

A majority of traditional model-matching tracking methods only consider the characteristics of the moving target without fully utilizing the relationship between the moving target and the image for object tracking,especially when the target was occluded during the process of object tracking.Consequently,these methods allow the moving target to drift easily;as a results,the moving target is sometimes lost.To solve these problems,a novel object-tracking approach based on constrained local model matching(CLMM)was proposed.First,the algorithm selects previous m frames of the image frame sequences for tracking training,and each image frame is divided into superpixel blocks.Second,the vector cluster is composed of all the superpixel blocks,and the object model that contains superpixel blocks is established by discrimination appearance model.Finally,the algorithm takes the object model as a matching model,adopts expectation maximization to estimate the foreground information,utilizes foreground discrimination to match the local model.Hence,the tracking object is determines.Compared with other excellent tracking algorithm,the proposed target-tracking algorithm can accurately and effectively adapt to complex changes in target states of a video scene through foreground discrimination and local model matching and can adequately solve the problems of tracking drift under various uncertain factors.This algorithm can also achieve the same or even higher tracking accuracy compared with existing model-matching tracking methods.Experiment results indicate that the proposed target-tracking algorithm can adaptively update noise model parameters in real time,accurately estimate the foreground information of images according to different image sequences,eliminate background information interference,achieve tracking accuracy and adaptability under the conditions of partial occlusion,target deformation,illumination changes,and complex background.