TY - GEN
T1 - Energy-efficient foreground object detection on embedded smart cameras by hardware-level operations
AU - Casares, Mauricio
AU - Santinelli, Paolo
AU - Velipasalar, Senem
AU - Prati, Andrea
AU - Cucchiara, Rita
PY - 2011
Y1 - 2011
N2 - Embedded smart cameras have limited processing power, memory and energy. In this paper, we introduce two methodologies to increase the energy-efficiency and the battery-life of an embedded smart camera by hardware-level operations when performing foreground object detection. We use the CITRIC platform as our embedded smart camera. We first perform down-sampling at hardware level on the micro-controller of the image sensor rather than performing software-level down-sampling at the main microprocessor of the camera board. In addition, we crop an image frame at hardware level by using the HREF and VSYNC signals at the micro-controller of the image sensor to perform foreground object detection only in the cropped search region instead of the whole image. Thus, the amount of data that is moved from the image sensor to the main memory at each frame, is greatly reduced. Thanks to reduced data transfer, better use of the memory resources and not occupying the main microprocessor with image down-sampling and cropping tasks, we obtain significant savings in energy consumption and battery-life. Experimental results show that hardware-level down-sampling and cropping, and performing detection in cropped regions provide 54.14% decrease in energy consumption, and 121.25% increase in battery-life compared to performing software-level down-sampling and processing whole frames.
AB - Embedded smart cameras have limited processing power, memory and energy. In this paper, we introduce two methodologies to increase the energy-efficiency and the battery-life of an embedded smart camera by hardware-level operations when performing foreground object detection. We use the CITRIC platform as our embedded smart camera. We first perform down-sampling at hardware level on the micro-controller of the image sensor rather than performing software-level down-sampling at the main microprocessor of the camera board. In addition, we crop an image frame at hardware level by using the HREF and VSYNC signals at the micro-controller of the image sensor to perform foreground object detection only in the cropped search region instead of the whole image. Thus, the amount of data that is moved from the image sensor to the main memory at each frame, is greatly reduced. Thanks to reduced data transfer, better use of the memory resources and not occupying the main microprocessor with image down-sampling and cropping tasks, we obtain significant savings in energy consumption and battery-life. Experimental results show that hardware-level down-sampling and cropping, and performing detection in cropped regions provide 54.14% decrease in energy consumption, and 121.25% increase in battery-life compared to performing software-level down-sampling and processing whole frames.
UR - http://www.scopus.com/inward/record.url?scp=80054906058&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80054906058&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2011.5981838
DO - 10.1109/CVPRW.2011.5981838
M3 - Conference contribution
AN - SCOPUS:80054906058
SN - 9781457705298
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 150
EP - 156
BT - 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011
PB - IEEE Computer Society
T2 - 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011
Y2 - 20 June 2011 through 25 June 2011
ER -