Research On Target Tracking Algorithm Based On Sparse Representation

Target tracking is to establish the position relationship of the objects to be tracked in a continuous video sequence and to obtain the complete trajectory of the object.Currently,it is widely used in military navigation,urban intelligent traffic control,video surveillance,and human-computer interaction.In the process of target movement,there will be changes in posture or shape,scale changes,background occlusion or changes in light brightness,etc.,which reduces the accuracy of the tracking algorithm,so building a highly efficient and stable adaptive tracking algorithm is a major problem in the field of current tracking.In order to solve the problem of target loss and improve tracking efficiency in tracking process,the following studies have been done:(1)To improve the calculation rate,reduce the interference of background information and enhance the performance of sparse representation tracking model,an anti-sparse tracking algorithm constructed using a piecewise weighting function is proposed,This algorithm converts the tracking problem into the candidate with the highest probability of finding the object under the Bayesian framework,measuring the discriminant feature coefficients of candidate targets and positive and negative templates respectively by constructing different segment weight functions,The pooling is used to reduce the uncertainty of tracking results,the candidate representation corresponding to the largest difference coefficient obtained by subtracting the positive template from the negative template is selected as the current tracking result.Four kinds of video sequences experiment tests have achieved better tracking results.(2)Aimming at the problems of partial information not being utilized,underutilizing background,foreground comparison information fully,and existing algorithms failing to discriminate between target and background in complex environments,an improved AdaBoost strong classifier algorithm was put forward which used local image blocks for discriminating model tracking,and can better distinguish the target and background and achieve more accurate target tracking.Tests show that the algorithm tracking performance is better.This paper summarizes and deeply studies the target tracking based on sparse representation,and improves the accuracy of target tracking to a certain extent.However,further study about how to extend the algorithm to multi-target tracking applications to improve the robustness of existing tracking algorithms is also needed.