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Silhouette coverage analysis for multi-modal video surveillance

Steven Verstockt (UGent) , Chris Poppe (UGent) , Pieterjan De Potter (UGent) , Charles Hollemeersch (UGent) , Sofie Van Hoecke (UGent) , Peter Lambert (UGent) and Rik Van de Walle (UGent)
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Abstract
In order to improve the accuracy in video-based object detection, the proposed multi-modal video surveillance system takes advantage of the different kinds of information represented by visual, thermal and/or depth imaging sensors. The multi-modal object detector of the system can be split up in two consecutive parts: the registration and the coverage analysis. The multi-modal image registration is performed using a three step silhouette-mapping algorithm which detects the rotation, scale and translation between moving objects in the visual, (thermal) infrared and/or depth images. First, moving object silhouettes are extracted to separate the calibration objects, i.e., the foreground, from the static background. Key components are dynamic background subtraction, foreground enhancement and automatic thresholding. Then, 1D contour vectors are generated from the resulting multi-modal silhouettes using silhouette boundary extraction, cartesian to polar transform and radial vector analysis. Next, to retrieve the rotation angle and the scale factor between the multi-sensor image, these contours are mapped on each other using circular cross correlation and contour scaling. Finally, the translation between the images is calculated using maximization of binary correlation. The silhouette coverage analysis also starts with moving object silhouette extraction. Then, it uses the registration information, i.e., rotation angle, scale factor and translation vector, to map the thermal, depth and visual silhouette images on each other. Finally, the coverage of the resulting multi-modal silhouette map is computed and is analyzed over time to reduce false alarms and to improve object detection. Prior experiments on real-world multi-sensor video sequences indicate that automated multi-modal video surveillance is promising. This paper shows that merging information from multi-modal video further increases the detection results.
Keywords
Video Surveillance

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Chicago
Verstockt, Steven, Chris Poppe, Pieterjan De Potter, Charles Hollemeersch, Sofie Van Hoecke, Peter Lambert, and Rik Van de Walle. 2011. “Silhouette Coverage Analysis for Multi-modal Video Surveillance.” In Progress in Electromagnetics Research Symposium, 1279–1283. Cambridge, MA, USA: The Electromagnetics Academy.
APA
Verstockt, S., Poppe, C., De Potter, P., Hollemeersch, C., Van Hoecke, S., Lambert, P., & Van de Walle, R. (2011). Silhouette coverage analysis for multi-modal video surveillance. Progress in Electromagnetics Research Symposium (pp. 1279–1283). Presented at the Progress in Electromagnetics Research Symposium (PIERS), Cambridge, MA, USA: The Electromagnetics Academy.
Vancouver
1.
Verstockt S, Poppe C, De Potter P, Hollemeersch C, Van Hoecke S, Lambert P, et al. Silhouette coverage analysis for multi-modal video surveillance. Progress in Electromagnetics Research Symposium. Cambridge, MA, USA: The Electromagnetics Academy; 2011. p. 1279–83.
MLA
Verstockt, Steven, Chris Poppe, Pieterjan De Potter, et al. “Silhouette Coverage Analysis for Multi-modal Video Surveillance.” Progress in Electromagnetics Research Symposium. Cambridge, MA, USA: The Electromagnetics Academy, 2011. 1279–1283. Print.
@inproceedings{1207419,
  abstract     = {In order to improve the accuracy in video-based object detection, the proposed multi-modal video surveillance system takes advantage of the different kinds of information represented by visual, thermal and/or depth imaging sensors.
 
The multi-modal object detector of the system can be split up in two consecutive parts: the registration and the coverage analysis. The multi-modal image registration is performed using a three step silhouette-mapping algorithm which detects the rotation, scale and translation between moving objects in the visual, (thermal) infrared and/or depth images. First, moving object silhouettes are extracted to separate the calibration objects, i.e., the foreground, from the static background. Key components are dynamic background subtraction, foreground enhancement and automatic thresholding. Then, 1D contour vectors are generated from the resulting multi-modal silhouettes using silhouette boundary extraction, cartesian to polar transform and radial vector analysis. Next, to retrieve the rotation angle and the scale factor between the multi-sensor image, these contours are mapped on each other using circular cross correlation and contour scaling. Finally, the translation between the images is calculated using maximization of binary correlation. 

The silhouette coverage analysis also starts with moving object silhouette extraction. Then, it uses the registration information, i.e., rotation angle, scale factor and translation vector, to map the thermal, depth and visual silhouette images on each other. Finally, the coverage of the resulting multi-modal silhouette map is computed and is analyzed over time to reduce false alarms and to improve object detection.
 
Prior experiments on real-world multi-sensor video sequences indicate that automated multi-modal video surveillance is promising. This paper shows that merging information from multi-modal video further increases the detection results.},
  author       = {Verstockt, Steven and Poppe, Chris and De Potter, Pieterjan and Hollemeersch, Charles and Van Hoecke, Sofie and Lambert, Peter and Van de Walle, Rik},
  booktitle    = {Progress in Electromagnetics Research Symposium},
  isbn         = {9781934142165},
  keyword      = {Video Surveillance},
  language     = {eng},
  location     = {Marrakesh, Marokko},
  pages        = {1279--1283},
  publisher    = {The Electromagnetics Academy},
  title        = {Silhouette coverage analysis for multi-modal video surveillance},
  year         = {2011},
}

Web of Science
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