Visual Tracking via Probability Continuous Outlier Model
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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Li, F., Cao, T., Song, Z., et al. (2015) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct.2015.7399968 · Cited by 1 · see citing works
Beyond appearance model: Learning appearance variations for object tracking
Li, G., Ma, B., Huang, J., et al. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.06.058 · Cited by 1 · see citing works
Feature Adaptive Correlation Tracking
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Han, Y., Liu, R., Zhong, G., et al. (2017) · Neural Computing and Applications · doi:10.1007/s00521-017-3031-7 · Cited by 1 · see citing works
RetractedDeep Activation Feature Maps for Visual Object Tracking
Li, Y., Miao, Z., & Wang, J. (2018) · doi:10.1145/3297067.3297088 · Cited by 1 · see citing works
Relaxed Target Representation for Visual Tracking
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Patch-based scale calculation for visual tracking
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Perspective model-based visual tracking scheme for robust tracking of objects in complex environs
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On large appearance change in visual tracking
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Robust Visual Tracking Based on Relaxed Target Representation
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Object tracking with improved adaptive differential evolution algorithm
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Online object tracking via bag-of-local-patches
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Self-citationVisual tracking via random partition image hashing
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Compressive Tracking based on Superpixel Segmentation
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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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