Robust visual tracking via rank-constrained sparse learning

Seyed Bozorgtabar, Roland GOECKE

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

In this paper, we present an improved low-rank sparse learning method for particle filter based visual tracking, which we denote as rank-constrained sparse learning. Since each particle can be sparsely represented by a linear combination of the bases from an adaptive dictionary, we exploit the underlying structure between particles by constraining the rank of particle sparse representations jointly over the adaptive dictionary. Besides utilising a common structure among particles, the proposed tracker also suggests the most discriminative features for particle representations using an additional feature selection module employed in the proposed objective function. Furthermore, we present an efficient way to solve this learning problem by connecting the low-rank structure extracted from particles to a simpler learning problem in the devised discriminative subspace. The suggested way improves the overall computational complexity for the high-dimensional particle candidates. Finally, in order to achieve a more robust tracker, we augment the sparse representation of particles with adaptive weights, which indicate similarity between candidates and the dictionary templates. The proposed approach is extensively evaluated on the VOT 2013 visual tracking evaluation platform including 16 challenging sequences. Experimental results compared to state-of-the-art methods show the robustness and effectiveness of the proposed tracker.
Original languageEnglish
Title of host publication2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2014)
Subtitle of host publicationTechniques and Applications, DICTA 2014
EditorsAbdesselam Bouzerdoum, Lei Wang, Philip Ogunbona, Wanqing Li, Son Lam Phung
Place of PublicationWollongong
PublisherIEEE
Pages1-7
Number of pages7
ISBN (Electronic)9781479954094
ISBN (Print)9781479954100
DOIs
Publication statusPublished - 25 Nov 2014
Event2014 International Conference on Digital Image Computing, Techniques and Applications - Wollongong, Wollongong, Australia
Duration: 25 Nov 201427 Nov 2014

Publication series

Name2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014

Conference

Conference2014 International Conference on Digital Image Computing, Techniques and Applications
Abbreviated titleDICTA 2014
CountryAustralia
CityWollongong
Period25/11/1427/11/14

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Glossaries
Feature extraction
Computational complexity

Cite this

Bozorgtabar, S., & GOECKE, R. (2014). Robust visual tracking via rank-constrained sparse learning. In A. Bouzerdoum, L. Wang, P. Ogunbona, W. Li, & S. L. Phung (Eds.), 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2014): Techniques and Applications, DICTA 2014 (pp. 1-7). [7008129] (2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014). Wollongong: IEEE. https://doi.org/10.1109/DICTA.2014.7008129
Bozorgtabar, Seyed ; GOECKE, Roland. / Robust visual tracking via rank-constrained sparse learning. 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2014): Techniques and Applications, DICTA 2014. editor / Abdesselam Bouzerdoum ; Lei Wang ; Philip Ogunbona ; Wanqing Li ; Son Lam Phung. Wollongong : IEEE, 2014. pp. 1-7 (2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014).
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abstract = "In this paper, we present an improved low-rank sparse learning method for particle filter based visual tracking, which we denote as rank-constrained sparse learning. Since each particle can be sparsely represented by a linear combination of the bases from an adaptive dictionary, we exploit the underlying structure between particles by constraining the rank of particle sparse representations jointly over the adaptive dictionary. Besides utilising a common structure among particles, the proposed tracker also suggests the most discriminative features for particle representations using an additional feature selection module employed in the proposed objective function. Furthermore, we present an efficient way to solve this learning problem by connecting the low-rank structure extracted from particles to a simpler learning problem in the devised discriminative subspace. The suggested way improves the overall computational complexity for the high-dimensional particle candidates. Finally, in order to achieve a more robust tracker, we augment the sparse representation of particles with adaptive weights, which indicate similarity between candidates and the dictionary templates. The proposed approach is extensively evaluated on the VOT 2013 visual tracking evaluation platform including 16 challenging sequences. Experimental results compared to state-of-the-art methods show the robustness and effectiveness of the proposed tracker.",
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Bozorgtabar, S & GOECKE, R 2014, Robust visual tracking via rank-constrained sparse learning. in A Bouzerdoum, L Wang, P Ogunbona, W Li & SL Phung (eds), 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2014): Techniques and Applications, DICTA 2014., 7008129, 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014, IEEE, Wollongong, pp. 1-7, 2014 International Conference on Digital Image Computing, Techniques and Applications, Wollongong, Australia, 25/11/14. https://doi.org/10.1109/DICTA.2014.7008129

Robust visual tracking via rank-constrained sparse learning. / Bozorgtabar, Seyed; GOECKE, Roland.

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2014): Techniques and Applications, DICTA 2014. ed. / Abdesselam Bouzerdoum; Lei Wang; Philip Ogunbona; Wanqing Li; Son Lam Phung. Wollongong : IEEE, 2014. p. 1-7 7008129 (2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014).

Research output: A Conference proceeding or a Chapter in BookConference contribution

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AU - GOECKE, Roland

PY - 2014/11/25

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N2 - In this paper, we present an improved low-rank sparse learning method for particle filter based visual tracking, which we denote as rank-constrained sparse learning. Since each particle can be sparsely represented by a linear combination of the bases from an adaptive dictionary, we exploit the underlying structure between particles by constraining the rank of particle sparse representations jointly over the adaptive dictionary. Besides utilising a common structure among particles, the proposed tracker also suggests the most discriminative features for particle representations using an additional feature selection module employed in the proposed objective function. Furthermore, we present an efficient way to solve this learning problem by connecting the low-rank structure extracted from particles to a simpler learning problem in the devised discriminative subspace. The suggested way improves the overall computational complexity for the high-dimensional particle candidates. Finally, in order to achieve a more robust tracker, we augment the sparse representation of particles with adaptive weights, which indicate similarity between candidates and the dictionary templates. The proposed approach is extensively evaluated on the VOT 2013 visual tracking evaluation platform including 16 challenging sequences. Experimental results compared to state-of-the-art methods show the robustness and effectiveness of the proposed tracker.

AB - In this paper, we present an improved low-rank sparse learning method for particle filter based visual tracking, which we denote as rank-constrained sparse learning. Since each particle can be sparsely represented by a linear combination of the bases from an adaptive dictionary, we exploit the underlying structure between particles by constraining the rank of particle sparse representations jointly over the adaptive dictionary. Besides utilising a common structure among particles, the proposed tracker also suggests the most discriminative features for particle representations using an additional feature selection module employed in the proposed objective function. Furthermore, we present an efficient way to solve this learning problem by connecting the low-rank structure extracted from particles to a simpler learning problem in the devised discriminative subspace. The suggested way improves the overall computational complexity for the high-dimensional particle candidates. Finally, in order to achieve a more robust tracker, we augment the sparse representation of particles with adaptive weights, which indicate similarity between candidates and the dictionary templates. The proposed approach is extensively evaluated on the VOT 2013 visual tracking evaluation platform including 16 challenging sequences. Experimental results compared to state-of-the-art methods show the robustness and effectiveness of the proposed tracker.

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M3 - Conference contribution

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BT - 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2014)

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A2 - Phung, Son Lam

PB - IEEE

CY - Wollongong

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Bozorgtabar S, GOECKE R. Robust visual tracking via rank-constrained sparse learning. In Bouzerdoum A, Wang L, Ogunbona P, Li W, Phung SL, editors, 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2014): Techniques and Applications, DICTA 2014. Wollongong: IEEE. 2014. p. 1-7. 7008129. (2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014). https://doi.org/10.1109/DICTA.2014.7008129