Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motion blur is pervasive in object tracking due to the camera and target movement. Most approaches are prone to drift when the target is blurred. Sparse representation of both normal and blur templates are able to improve the robustness of the appearance model, but how to update the template set remains a difficult problem. To solve this prob-lem,  propose a two-step observation correction strategy for template updating by: 1) treating motion blur as trivi-al information with Laplace distribution and using different ways to correct the normal target and the blur target; 2) utilizing incremental principle component analysis (PCA) to update the normal template set. Experiments on challenging videos show the proposed algorithm outperforms several state-of-the-art methods.
['Xiaofen Xing', 'Nanhai Zhang', 'Kailing Guo', 'Chunmei Qing', 'Jiali Deng', 'Huiping Qin']
I am writing a research paper on a idea Blurred target tracking based on sparse representation of online updated templates
[{"from": "human", "value": "I am writing a research paper on a idea Blurred target tracking based on sparse representation of online updated templates"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motion blur is pervasive in object tracking due to the camera and target movement. Most approaches are prone to drift when the target is blurred. Sparse representation of both normal and blur templates are able to improve the robustness of the appearance model, but how to update the template set remains a difficult problem. To solve this prob-lem,  propose a two-step observation correction strategy for template updating by: 1) treating motion blur as trivi-al information with Laplace distribution and using different ways to correct the normal target and the blur target; 2) utilizing incremental principle component analysis (PCA) to update the normal template set. Experiments on challenging videos show the proposed algorithm outperforms several state-of-the-art methods."}]