Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/617248
Title: Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking
Authors: Chenglong Li;Hui Cheng;Shiyi Hu;Xiaobai Liu;Jin Tang;Liang Lin
subject: grayscale-thermal tracking benchmark|Collaborative sparse representation|multi-task modeling|adaptive tracking
Year: 2016
Publisher: IEEE
Abstract: Integrating multiple different yet complementary feature representations has been proved to be an effective way for boosting tracking performance. This paper investigates how to perform robust object tracking in challenging scenarios by adaptively incorporating information from grayscale and thermal videos, and proposes a novel collaborative algorithm for online tracking. In particular, an adaptive fusion scheme is proposed based on collaborative sparse representation in Bayesian filtering framework. We jointly optimize sparse codes and the reliable weights of different modalities in an online way. In addition, this paper contributes a comprehensive video benchmark, which includes 50 grayscale-thermal sequences and their ground truth annotations for tracking purpose. The videos are with high diversity and the annotations were finished by one single person to guarantee consistency. Extensive experiments against other state-of-the-art trackers with both grayscale and grayscale-thermal inputs demonstrate the effectiveness of the proposed tracking approach. Through analyzing quantitative results, we also provide basic insights and potential future research directions in grayscale-thermal tracking.
URI: http://localhost/handle/Hannan/148095
http://localhost/handle/Hannan/617248
ISSN: 1057-7149
1941-0042
volume: 25
issue: 12
Appears in Collections:2016

Files in This Item:
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Title: Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking
Authors: Chenglong Li;Hui Cheng;Shiyi Hu;Xiaobai Liu;Jin Tang;Liang Lin
subject: grayscale-thermal tracking benchmark|Collaborative sparse representation|multi-task modeling|adaptive tracking
Year: 2016
Publisher: IEEE
Abstract: Integrating multiple different yet complementary feature representations has been proved to be an effective way for boosting tracking performance. This paper investigates how to perform robust object tracking in challenging scenarios by adaptively incorporating information from grayscale and thermal videos, and proposes a novel collaborative algorithm for online tracking. In particular, an adaptive fusion scheme is proposed based on collaborative sparse representation in Bayesian filtering framework. We jointly optimize sparse codes and the reliable weights of different modalities in an online way. In addition, this paper contributes a comprehensive video benchmark, which includes 50 grayscale-thermal sequences and their ground truth annotations for tracking purpose. The videos are with high diversity and the annotations were finished by one single person to guarantee consistency. Extensive experiments against other state-of-the-art trackers with both grayscale and grayscale-thermal inputs demonstrate the effectiveness of the proposed tracking approach. Through analyzing quantitative results, we also provide basic insights and potential future research directions in grayscale-thermal tracking.
URI: http://localhost/handle/Hannan/148095
http://localhost/handle/Hannan/617248
ISSN: 1057-7149
1941-0042
volume: 25
issue: 12
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7577747.pdf3.95 MBAdobe PDFThumbnail
Preview File
Title: Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking
Authors: Chenglong Li;Hui Cheng;Shiyi Hu;Xiaobai Liu;Jin Tang;Liang Lin
subject: grayscale-thermal tracking benchmark|Collaborative sparse representation|multi-task modeling|adaptive tracking
Year: 2016
Publisher: IEEE
Abstract: Integrating multiple different yet complementary feature representations has been proved to be an effective way for boosting tracking performance. This paper investigates how to perform robust object tracking in challenging scenarios by adaptively incorporating information from grayscale and thermal videos, and proposes a novel collaborative algorithm for online tracking. In particular, an adaptive fusion scheme is proposed based on collaborative sparse representation in Bayesian filtering framework. We jointly optimize sparse codes and the reliable weights of different modalities in an online way. In addition, this paper contributes a comprehensive video benchmark, which includes 50 grayscale-thermal sequences and their ground truth annotations for tracking purpose. The videos are with high diversity and the annotations were finished by one single person to guarantee consistency. Extensive experiments against other state-of-the-art trackers with both grayscale and grayscale-thermal inputs demonstrate the effectiveness of the proposed tracking approach. Through analyzing quantitative results, we also provide basic insights and potential future research directions in grayscale-thermal tracking.
URI: http://localhost/handle/Hannan/148095
http://localhost/handle/Hannan/617248
ISSN: 1057-7149
1941-0042
volume: 25
issue: 12
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7577747.pdf3.95 MBAdobe PDFThumbnail
Preview File