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Visual object tracking via coefficients constrained exclusive group LASSO


Discriminative methods have been widely applied to construct the appearance model for visual tracking. Most existing methods incorporate online updating strategy to adapt to the appearance variations of targets. The focus of online updating for discriminative methods is to select the positive samples emerged in past frames to represent the appearances. However, the appearances of positive samples might be very dissimilar to each other; traditional online updating strategies easily overfit on some appearances and neglect the others. To address this problem, we propose an effective method to learn a discriminative template, which maintains the multiple appearances information of targets in the long-term variations. Our method is based on the obvious observation that the target appearances vary very little in a certain number of successive video frames. Therefore, we can use a few instances to represent the appearances in the scope of the successive video frames. We propose exclusive group sparse to describe the observation and provide a novel algorithm, called coefficients constrained exclusive group LASSO, to solve it in a single objective function. The experimental results on CVPR2013 benchmark datasets demonstrate that our approach achieves promising performance.

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This work is supported by the National Natural Science Foundation of China (Nos. 61762021, 61402122, 61672183, 61272252, 61401228, 61461008), Science and Technology Planning Project of Guanddong Province (No. 2016B090918047), Shenzhen Research Council (No.JCYJ20160406161948211, JCYJ2016022 6201453085, JSGG20150331152017052), Natural Science Foundation of Guangdong Province (No. 2015A030313544), the 2014 Ph.D. Recruitment Program of Guizhou Normal University, Natural Science Foundation of Guizhou Province (No. 2017[1130]), the Outstanding Innovation Talents of Science and Technology Award Scheme of Education Department in Guizhou Province (Qian jiao KY word[2015]487), the China Scholarship Council (No. 201508525007), Fund of Guizhou Educational Department (KY[2016]027), China Postdoctoral Science Foundation (Grant No. 2015M581841) and Postdoctoral Science Foundation of Jiangsu Province (Grant No. 1501019A).

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Correspondence to Weihua Ou.

Additional information

Xiao Ma and Qiao Liu contributed equally to this work and should be considered co-first authors. Weihua Ou and Quan Zhou are the corresponding authors.

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Ma, X., Liu, Q., Ou, W. et al. Visual object tracking via coefficients constrained exclusive group LASSO. Machine Vision and Applications 29, 749–763 (2018). https://doi.org/10.1007/s00138-018-0930-2

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  • Discriminative methods
  • Samples selection
  • Template matching
  • Exclusive group LASSO