Unifying spatial and attribute selection for distracter-resilient tracking

Nan Jiang*, Ying Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Visual distracters are detrimental and generally very difficult to handle in target tracking, because they generate false positive candidates for target matching. The resilience of region-based matching to the distracters depends not only on the matching metric, but also on the characteristics of the target region to be matched. The two tasks, i.e., learning the best metric and selecting the distracter-resilient target regions, actually correspond to the attribute selection and spatial selection processes in the human visual perception. This paper presents an initial attempt to unify the modeling of these two tasks for an effective solution, based on the introduction of a new quantity called Soft Visual Margin. As a function of both matching metric and spatial location, it measures the discrimination between the target and its spatial distracters, and characterizes the reliability of matching. Different from other formulations of margin, this new quantity is analytical and is insensitive to noisy data. This paper presents a novel method to jointly determine the best spatial location and the optimal metric. Based on that, a solid distracter-resilient region tracker is designed, and its effectiveness is validated and demonstrated through extensive experiments.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Pages3502-3509
Number of pages8
ISBN (Electronic)9781479951178, 9781479951178
DOIs
StatePublished - Sep 24 2014
Event27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States
Duration: Jun 23 2014Jun 28 2014

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
CountryUnited States
CityColumbus
Period6/23/146/28/14

Keywords

  • attribute adjustment
  • spatial adjustment
  • visual tracking

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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  • Cite this

    Jiang, N., & Wu, Y. (2014). Unifying spatial and attribute selection for distracter-resilient tracking. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 3502-3509). [6909843] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). IEEE Computer Society. https://doi.org/10.1109/CVPR.2014.448