Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/141659
Title: Weighted Low-Rank Decomposition for Robust Grayscale-Thermal Foreground Detection
Authors: Chenglong Li;Xiao Wang;Lei Zhang;Jin Tang;Hejun Wu;Liang Lin
Year: 2017
Publisher: IEEE
Abstract: This paper investigates how to fuse grayscale and thermal video data for detecting foreground objects in challenging scenarios. To this end, we propose an intuitive yet effective method called weighted low-rank decomposition (WELD), which adaptively pursues the cross-modality low-rank representation. Specifically, we form two data matrices by accumulating sequential frames from the grayscale and the thermal videos, respectively. Within these two observing matrices, WELD detects moving foreground pixels as sparse outliers against the low-rank structure background and incorporates the weight variables to make the models of two modalities complementary to each other. The smoothness constraints of object motion are also introduced in WELD to further improve the robustness to noises. For optimization, we propose an iterative algorithm to efficiently solve the low-rank models with three subproblems. Moreover, we utilize an edge-preserving filtering-based method to substantially speed up WELD while preserving its accuracy. To provide a comprehensive evaluation benchmark of grayscale-thermal foreground detection, we create a new data set including 25 aligned grayscale-thermal video pairs with high diversity. Our extensive experiments on both the newly created data set and the public data set OSU3 suggest that WELD achieves superior performance and comparable efficiency against other state-of-the-art approaches.
URI: http://localhost/handle/Hannan/141659
volume: 27
issue: 4
More Information: 725,
738
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7457366.pdf4.97 MBAdobe PDF
Title: Weighted Low-Rank Decomposition for Robust Grayscale-Thermal Foreground Detection
Authors: Chenglong Li;Xiao Wang;Lei Zhang;Jin Tang;Hejun Wu;Liang Lin
Year: 2017
Publisher: IEEE
Abstract: This paper investigates how to fuse grayscale and thermal video data for detecting foreground objects in challenging scenarios. To this end, we propose an intuitive yet effective method called weighted low-rank decomposition (WELD), which adaptively pursues the cross-modality low-rank representation. Specifically, we form two data matrices by accumulating sequential frames from the grayscale and the thermal videos, respectively. Within these two observing matrices, WELD detects moving foreground pixels as sparse outliers against the low-rank structure background and incorporates the weight variables to make the models of two modalities complementary to each other. The smoothness constraints of object motion are also introduced in WELD to further improve the robustness to noises. For optimization, we propose an iterative algorithm to efficiently solve the low-rank models with three subproblems. Moreover, we utilize an edge-preserving filtering-based method to substantially speed up WELD while preserving its accuracy. To provide a comprehensive evaluation benchmark of grayscale-thermal foreground detection, we create a new data set including 25 aligned grayscale-thermal video pairs with high diversity. Our extensive experiments on both the newly created data set and the public data set OSU3 suggest that WELD achieves superior performance and comparable efficiency against other state-of-the-art approaches.
URI: http://localhost/handle/Hannan/141659
volume: 27
issue: 4
More Information: 725,
738
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7457366.pdf4.97 MBAdobe PDF
Title: Weighted Low-Rank Decomposition for Robust Grayscale-Thermal Foreground Detection
Authors: Chenglong Li;Xiao Wang;Lei Zhang;Jin Tang;Hejun Wu;Liang Lin
Year: 2017
Publisher: IEEE
Abstract: This paper investigates how to fuse grayscale and thermal video data for detecting foreground objects in challenging scenarios. To this end, we propose an intuitive yet effective method called weighted low-rank decomposition (WELD), which adaptively pursues the cross-modality low-rank representation. Specifically, we form two data matrices by accumulating sequential frames from the grayscale and the thermal videos, respectively. Within these two observing matrices, WELD detects moving foreground pixels as sparse outliers against the low-rank structure background and incorporates the weight variables to make the models of two modalities complementary to each other. The smoothness constraints of object motion are also introduced in WELD to further improve the robustness to noises. For optimization, we propose an iterative algorithm to efficiently solve the low-rank models with three subproblems. Moreover, we utilize an edge-preserving filtering-based method to substantially speed up WELD while preserving its accuracy. To provide a comprehensive evaluation benchmark of grayscale-thermal foreground detection, we create a new data set including 25 aligned grayscale-thermal video pairs with high diversity. Our extensive experiments on both the newly created data set and the public data set OSU3 suggest that WELD achieves superior performance and comparable efficiency against other state-of-the-art approaches.
URI: http://localhost/handle/Hannan/141659
volume: 27
issue: 4
More Information: 725,
738
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7457366.pdf4.97 MBAdobe PDF