Research On Object Low-Rank Tracking Algorithm Based On Compressed Feature

In recent years,the development of the GPU and parallel computing greatly enhance the ability of computer image processing,computer vision has been gradually from research to application technology development.Visual target tracking is an important direction in the field of computer vision,and in military defense and civilian aspects play an important role,the robust tracking problem under complex scene is still need to solve.In this paper,we mainly research a tracking method based on compressed sensing and low-rank matrix decomposition.Compressed sensing is a research hotspot in the field of image processing,it is mainly used for large-scale data analysis and processing.Compressed sensing tracking method extract image compressed domain features,adopts a classifier of compressed domain feature to classification to determine target estimation.Compressed sensing tracking need high quality features,but classifier is difficult to distinguish prospects for use lack understanding features.The same target in different time has similarity,this similarity can be seen as the low rank of image matrix,low-rank matrix sparse decomposition research how to restore the low rank of a data matrix.Low-rank matrix sparse decomposition target tracking as a template with the target in each moment to find the most similar regional problems.Low-rank matrix sparse decomposition tracking is better than Compressed sensing tracking in quality of features,but poor real-time.Based on advantages and disadvantages of both methods,we combine compressed sensing with sparse and low-rank matrix decomposition to complete the single-target tracking task.In our algorithm the classifier in compressed sensing tracker is replaced by sparse and low-rank matrix decomposition,using inexact augmented lagrange multiplier method to decompose the target appearance model,weak the tracker requirement for the quality of target features.We use compressed sensing of original vector dimension reduction to retain the original space information of low dimensional feature space,thus reducing the matrix calculation of sparse and low-rank matrix decomposition.In order to solve target tracking under scene with occlusion,fast motion and illumination problems,a dictionary update method is proposed in this paper.Our dictionary update method overcome the problem of tracking robustness.Based on this,we improved the algorithm real-time and robustness with vector similarity discrimination,trajectory correction,a new method for the observation matrix form.We compare our tracking algorithm with several popular algorithm.The experiment evaluations demonstrate that our algorithm can track objects in complex scene with occlusion,fast motion and illumination changes.