Object Tracking Based on Sparse Discriminative and Generative Model
Keywords:
Object tracking, Target feature modelling, sparsity-based generative model, sparsity-based discriminative classifierAbstract
Real time object tracking is a challenging task in computer vision. Many algorithms exist in literature like mean shift method, kernel method , pixel based, Silhouette based and sparity based, method. Of these methods robust appearance model that exploits both holistic templates and local representations is the sparsity-based discriminative classifier (SDC) and a sparsity-based generative model (SGM). SDC module, is effective method to compute the confidence value that assigns more weights to the foreground than the background in the SGM module. Further the histogram-based method is also discussed that takes the spatial information of each patch into consideration with an occlusion handing scheme. Furthermore, the update scheme considers both the latest observations and the original template, thereby enabling the tracker to deal with appearance change effectively. Experimental results show that the above method gives good performance and accuracy even in the presence of occlusion.
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