Cited by 113Video Surveillance and Tracking MethodsAdvanced Vision and ImagingAutonomous Vehicle Technology and Safety

Visual Tracking via Probability Continuous Outlier Model

Wang, Dong, Lu, Huchuan·2014·2014 IEEE Conference on Computer Vision and Pattern Recognition

Abstract

In this paper, we present a novel online visual tracking method based on linear representation. First, we present a novel probability continuous outlier model (PCOM) to depict the continuous outliers that occur in the linear representation model. In the proposed model, the element of the noisy observation sample can be either represented by a PCA subspace with small Guassian noise or treated as an arbitrary value with a uniform prior, in which the spatial consistency prior is exploited by using a binary Markov random field model. Then, we derive the objective function of the PCOM method, the solution of which can be iteratively obtained by the outlier-free least squares and standard max-flow/min-cut steps. Finally, based on the proposed PCOM method, we design an effective observation likelihood function and a simple update scheme for visual tracking. Both qualitative and quantitative evaluations demonstrate that our tracker achieves very favorable performance in terms of both accuracy and speed.

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Crossref · 10.1109/cvpr.2014.445

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Authors
Wang, D., & Lu, H.
Affiliations
Dalian University of Technology (CN)
Venue
2014 IEEE Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2014-06
Pages
3478-3485
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2036502561
Cited by
113
References
30
Citations by year
2023: 1 · 2022: 3 · 2021: 7 · 2020: 5 · 2019: 10 · 2018: 18 · 2017: 19 · 2016: 31 · 2015: 19
FWCI
12.37
Type
conference paper
Language
en
Open access
closed
Locations
IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Keywords
Gaussian noise, uniform prior, binary Markov random fields, max-flow min-cut theorem, target occlusion

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