Abstract
In the most tracking approaches, a score function is utilized to determine which candidate is the optimal one by measuring the similarity between the candidate and the template. However, the representative samples selection in the template update is challenging. To address this problem, in this paper, we treat the template as a linear combination of representative samples and propose a novel approach to select representative samples based on the coefficient constrained model. We formulate the objective function into a non-negative least square problem and obtain the solution utilizing standard non-negative least square. The experimental results show that the observation module of our approach outperforms several other observation modules under the same feature and motion module, such as support vector machine, logistic regression, ridge regression and structured outputs support vector machine.
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BlurBody, BlurOwl, Car1, Freeman1, Freeman3, Freeman4, Human2, Human3, Human6, Human7, Human8, Human9, Lemming, Jogging-1, Jogging-2 and Suv.
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Acknowledgments
We sincerely thank Xiao Ma and Quan Zhou for their good suggestions and discussion for the improvement of this paper. This work was supported by the National Nature Science Foundation of China (No. 61402122, 61461008, 61672183, 61272252), the 2014 Ph.D. Recruitment Program of Guizhou Normal University, the Outstanding Innovation Talents of Science and Technology Award Scheme of Education Department in Guizhou Province (Qianjiao KY word [2015]487), Fund of Guizhou educational department (KY[2016]027), the China Scholarship Council (No.201508525007), the Natural Science Foundation of Guangdong Province (Grant No. 2015A030313544), and the Shenzhen Research Council(Grant No. JCYJ20160406161948211, JCYJ20160226201453085).
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Weihua Ou and Di Yuan contributed equally to this work and should be considered co-first authors
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Ou, W., Yuan, D., Liu, Q. et al. Object tracking based on online representative sample selection via non-negative least square. Multimed Tools Appl 77, 10569–10587 (2018). https://doi.org/10.1007/s11042-017-4672-3
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DOI: https://doi.org/10.1007/s11042-017-4672-3