Research Of Object Tracking Based On Sparse Representation

Visual tracking is a hot research topic in the field of computer vision,especially for military reconnaissance,medical imaging,robotics and human-computer interaction.Although many tracking methods have been proposed in recent years,it is still a challenging problem to develop a robust algorithm for complex and dynamic scenes due to large appearance change caused by varying illumination,camera motion,occlusions,pose variation and shape deformation.The main contents of this paper are as follows:Firstly,as the main challenge for object tracking is to account for drastic appearance change.In this paper,we develop a sparsity-based discriminative classifier(SDC)and a sparsity-based generative model(SGM).Secondly,in the SDC module,we introduce an effective method to compute the confidence value that assigns more weights to the foreground than the background.In the SGM module,we propose a novel average pooling scheme that takes the spatial information of each patch into consideration with an occlusion handing scheme.The similarity obtained by pooling across the local patches helps not only locate the target more accurately but also handle occlusion.Thirdly,to the purpose of reducing the time of l1 method,we combined the collaborative representation(CR)method to constructing a robust object tracking based on collaborative model via l2 norm minimization algorithm.This algorithm can ensure the effectiveness of the algorithm and greatly reduce the complexity.Finally,experiments on some challenge video sequences demonstrate that our proposed tracker is robust and effective to challenge issues such as illumination change,clutter background partial occlusion and so on and perform favorably against state-of-art algorithms.