Visual tracking via salient feature extraction and sparse collaborative model |
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| Affiliation: | 1. School of Computer and Electronics and Information, Guangxi University, Nanning, Guangxi 530004, PR China;2. School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510006, PR China;3. Guangxi Key Laboratory of Multimedia Communications Network Technology, Guangxi University, Nanning, Guangxi 530004, PR China;1. School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China;2. School of Information Science and Engineering, Southeast University, Nanjing, 210096, China;3. School of Information Engineering, Qingdao University, Qingdao, 266000, China;4. Concordia University, Quebec, Canada;1. Electronic Information School of Wuhan University, Wuhan 430072, China;2. Shenzhen Institute of Wuhan University, Shenzhen 518057, China;3. The 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 210007, China |
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| Abstract: | Object tracking is always a very attractive research topic in computer vision and image processing. In this paper, an innovative method called salient-sparse-collaborative tracker (SSCT) is put forward, which exploits both object saliency and sparse representation. Within the proposed collaborative appearance model, the object salient feature map is built to create a salient-sparse discriminative model (SSDM) and a salient-sparse generative model (SSGM). In the SSDM module, the presented sparse model effectively distinguishes the target region from its background by using the salient feature map that further helps locate the object in complex environment. In the SSGM module, a sparse representation method with salient feature map is designed to improve the effectiveness of the templates and deal with occlusions. The update scheme takes advantage of salient correction, thus the SSCT algorithm can both handle the appearance variation as well as reduce tracking drifts effectively. Plenty of experiments with quantitative and qualitative comparisons on benchmark reveal the SSCT tracker is more competitive than several popular approaches. |
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| Keywords: | Salient feature Sparse representation Collaborative model Visual tracking |
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