Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/193208
Title: Grayscale-Thermal Object Tracking via Multitask Laplacian Sparse Representation
Authors: Chenglong Li;Xiang Sun;Xiao Wang;Lei Zhang;Jin Tang
Year: 2017
Publisher: IEEE
Abstract: This paper studies the problem of object tracking in challenging scenarios by leveraging multimodal visual data. We propose a grayscale-thermal object tracking method in Bayesian filtering framework based on multitask Laplacian sparse representation. Given one bounding box, we extract a set of overlapping local patches within it, and pursue the multitask joint sparse representation for grayscale and thermal modalities. Then, the representation coefficients of the two modalities are concatenated into a vector to represent the feature of the bounding box. Moreover, the similarity between each patch pair is deployed to refine their representation coefficients in the sparse representation, which can be formulated as the Laplacian sparse representation. We also incorporate the modal reliability into the Laplacian sparse representation to achieve an adaptive fusion of different source data. Experiments on two grayscale-thermal datasets suggest that the proposed approach outperforms both grayscale and grayscale-thermal tracking approaches.
URI: http://localhost/handle/Hannan/193208
volume: 47
issue: 4
More Information: 673,
681
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7822984.pdf1.43 MBAdobe PDF
Title: Grayscale-Thermal Object Tracking via Multitask Laplacian Sparse Representation
Authors: Chenglong Li;Xiang Sun;Xiao Wang;Lei Zhang;Jin Tang
Year: 2017
Publisher: IEEE
Abstract: This paper studies the problem of object tracking in challenging scenarios by leveraging multimodal visual data. We propose a grayscale-thermal object tracking method in Bayesian filtering framework based on multitask Laplacian sparse representation. Given one bounding box, we extract a set of overlapping local patches within it, and pursue the multitask joint sparse representation for grayscale and thermal modalities. Then, the representation coefficients of the two modalities are concatenated into a vector to represent the feature of the bounding box. Moreover, the similarity between each patch pair is deployed to refine their representation coefficients in the sparse representation, which can be formulated as the Laplacian sparse representation. We also incorporate the modal reliability into the Laplacian sparse representation to achieve an adaptive fusion of different source data. Experiments on two grayscale-thermal datasets suggest that the proposed approach outperforms both grayscale and grayscale-thermal tracking approaches.
URI: http://localhost/handle/Hannan/193208
volume: 47
issue: 4
More Information: 673,
681
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7822984.pdf1.43 MBAdobe PDF
Title: Grayscale-Thermal Object Tracking via Multitask Laplacian Sparse Representation
Authors: Chenglong Li;Xiang Sun;Xiao Wang;Lei Zhang;Jin Tang
Year: 2017
Publisher: IEEE
Abstract: This paper studies the problem of object tracking in challenging scenarios by leveraging multimodal visual data. We propose a grayscale-thermal object tracking method in Bayesian filtering framework based on multitask Laplacian sparse representation. Given one bounding box, we extract a set of overlapping local patches within it, and pursue the multitask joint sparse representation for grayscale and thermal modalities. Then, the representation coefficients of the two modalities are concatenated into a vector to represent the feature of the bounding box. Moreover, the similarity between each patch pair is deployed to refine their representation coefficients in the sparse representation, which can be formulated as the Laplacian sparse representation. We also incorporate the modal reliability into the Laplacian sparse representation to achieve an adaptive fusion of different source data. Experiments on two grayscale-thermal datasets suggest that the proposed approach outperforms both grayscale and grayscale-thermal tracking approaches.
URI: http://localhost/handle/Hannan/193208
volume: 47
issue: 4
More Information: 673,
681
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7822984.pdf1.43 MBAdobe PDF