Enhanced Laplacian Group Sparse Learning with Lifespan Outlier Rejection for Visual Tracking

Seyed Bozorgtabar, Roland GOECKE

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

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Abstract

Recently, sparse based learning methods have attracted much attention in robust visual tracking due to their effectiveness and promising tracking results. By representing the target object sparsely, utilising only a few adaptive dictionary templates, in this paper, we introduce a new particle filter based tracking method, in which we aim to capture the underlying structure among the particle samples using the proposed similarity graph in a Laplacian group sparse framework, such that the tracking results can be improved. Furthermore, in our tracker, particles contribute with different probabilities in the tracking result with respect to their relative positions in a given frame in regard to the current target object location. In addition, since the new target object can be well modelled by the most recent tracking results, we prefer to utilise the particle samples that are highly associated to the preceding tracking results. We demonstrate that the proposed formulation can be efficiently solved using the Accelerated Proximal method with just a small number of iterations. The proposed approach has been extensively evaluated on 12 challenging video sequences. Experimental results compared to the state-of-the-art methods demonstrate the merits of the proposed tracker.
Original languageEnglish
Title of host publication12th Asian Conference on Computer Vision (ACCV 2014)
Subtitle of host publicationLecture Notes in Computer Science
EditorsDaniel Cremers, Hideo Saito, Ian Reid, Ming-Hsuan Yang
Place of PublicationSwitzerland
PublisherSpringer
Pages564-578
Number of pages15
Volume9007
ISBN (Electronic)9783319168135
ISBN (Print)9783319168135
DOIs
Publication statusPublished - 2015
Event12th Asian Conference on Computer Vision - Singapore, Singapore, Singapore
Duration: 1 Nov 20145 Nov 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9007
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th Asian Conference on Computer Vision
CountrySingapore
CitySingapore
Period1/11/145/11/14

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Bozorgtabar, S., & GOECKE, R. (2015). Enhanced Laplacian Group Sparse Learning with Lifespan Outlier Rejection for Visual Tracking. In D. Cremers, H. Saito, I. Reid, & M-H. Yang (Eds.), 12th Asian Conference on Computer Vision (ACCV 2014): Lecture Notes in Computer Science (Vol. 9007, pp. 564-578). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9007). Switzerland: Springer. https://doi.org/10.1007/978-3-319-16814-2_37
Bozorgtabar, Seyed ; GOECKE, Roland. / Enhanced Laplacian Group Sparse Learning with Lifespan Outlier Rejection for Visual Tracking. 12th Asian Conference on Computer Vision (ACCV 2014): Lecture Notes in Computer Science. editor / Daniel Cremers ; Hideo Saito ; Ian Reid ; Ming-Hsuan Yang. Vol. 9007 Switzerland : Springer, 2015. pp. 564-578 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "Recently, sparse based learning methods have attracted much attention in robust visual tracking due to their effectiveness and promising tracking results. By representing the target object sparsely, utilising only a few adaptive dictionary templates, in this paper, we introduce a new particle filter based tracking method, in which we aim to capture the underlying structure among the particle samples using the proposed similarity graph in a Laplacian group sparse framework, such that the tracking results can be improved. Furthermore, in our tracker, particles contribute with different probabilities in the tracking result with respect to their relative positions in a given frame in regard to the current target object location. In addition, since the new target object can be well modelled by the most recent tracking results, we prefer to utilise the particle samples that are highly associated to the preceding tracking results. We demonstrate that the proposed formulation can be efficiently solved using the Accelerated Proximal method with just a small number of iterations. The proposed approach has been extensively evaluated on 12 challenging video sequences. Experimental results compared to the state-of-the-art methods demonstrate the merits of the proposed tracker.",
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Bozorgtabar, S & GOECKE, R 2015, Enhanced Laplacian Group Sparse Learning with Lifespan Outlier Rejection for Visual Tracking. in D Cremers, H Saito, I Reid & M-H Yang (eds), 12th Asian Conference on Computer Vision (ACCV 2014): Lecture Notes in Computer Science. vol. 9007, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9007, Springer, Switzerland, pp. 564-578, 12th Asian Conference on Computer Vision, Singapore, Singapore, 1/11/14. https://doi.org/10.1007/978-3-319-16814-2_37

Enhanced Laplacian Group Sparse Learning with Lifespan Outlier Rejection for Visual Tracking. / Bozorgtabar, Seyed; GOECKE, Roland.

12th Asian Conference on Computer Vision (ACCV 2014): Lecture Notes in Computer Science. ed. / Daniel Cremers; Hideo Saito; Ian Reid; Ming-Hsuan Yang. Vol. 9007 Switzerland : Springer, 2015. p. 564-578 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9007).

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

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AU - GOECKE, Roland

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N2 - Recently, sparse based learning methods have attracted much attention in robust visual tracking due to their effectiveness and promising tracking results. By representing the target object sparsely, utilising only a few adaptive dictionary templates, in this paper, we introduce a new particle filter based tracking method, in which we aim to capture the underlying structure among the particle samples using the proposed similarity graph in a Laplacian group sparse framework, such that the tracking results can be improved. Furthermore, in our tracker, particles contribute with different probabilities in the tracking result with respect to their relative positions in a given frame in regard to the current target object location. In addition, since the new target object can be well modelled by the most recent tracking results, we prefer to utilise the particle samples that are highly associated to the preceding tracking results. We demonstrate that the proposed formulation can be efficiently solved using the Accelerated Proximal method with just a small number of iterations. The proposed approach has been extensively evaluated on 12 challenging video sequences. Experimental results compared to the state-of-the-art methods demonstrate the merits of the proposed tracker.

AB - Recently, sparse based learning methods have attracted much attention in robust visual tracking due to their effectiveness and promising tracking results. By representing the target object sparsely, utilising only a few adaptive dictionary templates, in this paper, we introduce a new particle filter based tracking method, in which we aim to capture the underlying structure among the particle samples using the proposed similarity graph in a Laplacian group sparse framework, such that the tracking results can be improved. Furthermore, in our tracker, particles contribute with different probabilities in the tracking result with respect to their relative positions in a given frame in regard to the current target object location. In addition, since the new target object can be well modelled by the most recent tracking results, we prefer to utilise the particle samples that are highly associated to the preceding tracking results. We demonstrate that the proposed formulation can be efficiently solved using the Accelerated Proximal method with just a small number of iterations. The proposed approach has been extensively evaluated on 12 challenging video sequences. Experimental results compared to the state-of-the-art methods demonstrate the merits of the proposed tracker.

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Bozorgtabar S, GOECKE R. Enhanced Laplacian Group Sparse Learning with Lifespan Outlier Rejection for Visual Tracking. In Cremers D, Saito H, Reid I, Yang M-H, editors, 12th Asian Conference on Computer Vision (ACCV 2014): Lecture Notes in Computer Science. Vol. 9007. Switzerland: Springer. 2015. p. 564-578. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-16814-2_37