An FPGA Implementation of Intelligent Visual Based Fall Detection
Falling has been one of the major concerns and threats
to the independence of the elderly in their daily lives. With the
worldwide significant growth of the aging population, it is essential
to have a promising solution of fall detection which is able to operate
at high accuracy in real-time and supports large scale implementation
using multiple cameras. Field Programmable Gate Array (FPGA) is a
highly promising tool to be used as a hardware accelerator in many
emerging embedded vision based system. Thus, it is the main
objective of this paper to present an FPGA-based solution of visual
based fall detection to meet stringent real-time requirements with
high accuracy. The hardware architecture of visual based fall
detection which utilizes the pixel locality to reduce memory accesses
is proposed. By exploiting the parallel and pipeline architecture of
FPGA, our hardware implementation of visual based fall detection
using FGPA is able to achieve a performance of 60fps for a series of
video analytical functions at VGA resolutions (640x480). The results
of this work show that FPGA has great potentials and impacts in
enabling large scale vision system in the future healthcare industry
due to its flexibility and scalability.
[1] U. Laessoe, H.C. Hoeck, O. Simonsen, Thomas Sinkjaer, Michael Voigt,
"Fall risk in an active elderly population can it be assessed?", Journal of
Negative Results in BioMedicine, 2007, 6:2.
[2] Rabieyah Mat, Hajar Md. Taha, "Socio-Economic Characteristics of the
Elderly in Malaysia", 21st Population Census Conference, 2003.
[3] Report on seniors-s falls in Canada. Public Health Agency of Canada,
Division of Aging and Seniors, 2005.
[4] Tao, M. Turjo, M. F. Wong, M. Wang, and Y. P. Tan, "Fall incidents
detection for intelligent video surveillance," in Proc. IEEE Int. Conf.
Commu. and Signal Processing, 2005, pp. 1590-1594.
[5] C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau, "Robust video
surveillance for fall detection based on human shape deformation,"
IEEE Transactions on Circuits and Systems for Video Technology, vol.
21, no. 5, May, 2011, pp. 611-622.
[6] Y. T. Chen, Y. C. Lin, and W. H. Fang, "A hybrid human fall detection
scheme," in Proc. of 2010 IEEE 17th International Conference on Image
Processing, 2010, pp. 3485-3488.
[7] C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau, "Fall detection
from human shape and motion history using video surveillance," in
Proc. 21st Int. Conf. AINAW, vol. 2. 2007, pp. 875-880.
[8] JiaLuen Chua, YoongChoon Chang, Wee Keong Lim, "Intelligent
Visual Based Fall Detection Technique for Home Surveillance,"
International Symposium on Computer, Consumer and Control (IS3C),
2012, pp. 183-187.
[9] Asano, S., Maruyama, T., Yamaguchi, Y., "Performance comparison of
FPGA, GPU and CPU in image processing," International Conference
on Field Programmable Logic and Applications, 2009, pp. 126-131.
[10] S.-C. Cheung and C. Kamath, "Robust techniques for background
subtraction in urban traffic video," in Proc. of the SPIE, vol. 5308, 2004,
pp. 881-892.
[11] A. Sanin, C. Sanderson, B.C. Lovell. "Shadow Detection: A Survey and
Comparative Evaluation of Recent Methods," Pattern Recognition, Vol.
45, No. 4, 2012, pp. 1684-1695.
[12] F. Kristensen, P. Nilsson, and V. Öwall, "Background segmentation
beyond RGB," in Proc. Asian Conf. Computer Vision, vol. 2, 2006, pp.
602-612.
[1] U. Laessoe, H.C. Hoeck, O. Simonsen, Thomas Sinkjaer, Michael Voigt,
"Fall risk in an active elderly population can it be assessed?", Journal of
Negative Results in BioMedicine, 2007, 6:2.
[2] Rabieyah Mat, Hajar Md. Taha, "Socio-Economic Characteristics of the
Elderly in Malaysia", 21st Population Census Conference, 2003.
[3] Report on seniors-s falls in Canada. Public Health Agency of Canada,
Division of Aging and Seniors, 2005.
[4] Tao, M. Turjo, M. F. Wong, M. Wang, and Y. P. Tan, "Fall incidents
detection for intelligent video surveillance," in Proc. IEEE Int. Conf.
Commu. and Signal Processing, 2005, pp. 1590-1594.
[5] C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau, "Robust video
surveillance for fall detection based on human shape deformation,"
IEEE Transactions on Circuits and Systems for Video Technology, vol.
21, no. 5, May, 2011, pp. 611-622.
[6] Y. T. Chen, Y. C. Lin, and W. H. Fang, "A hybrid human fall detection
scheme," in Proc. of 2010 IEEE 17th International Conference on Image
Processing, 2010, pp. 3485-3488.
[7] C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau, "Fall detection
from human shape and motion history using video surveillance," in
Proc. 21st Int. Conf. AINAW, vol. 2. 2007, pp. 875-880.
[8] JiaLuen Chua, YoongChoon Chang, Wee Keong Lim, "Intelligent
Visual Based Fall Detection Technique for Home Surveillance,"
International Symposium on Computer, Consumer and Control (IS3C),
2012, pp. 183-187.
[9] Asano, S., Maruyama, T., Yamaguchi, Y., "Performance comparison of
FPGA, GPU and CPU in image processing," International Conference
on Field Programmable Logic and Applications, 2009, pp. 126-131.
[10] S.-C. Cheung and C. Kamath, "Robust techniques for background
subtraction in urban traffic video," in Proc. of the SPIE, vol. 5308, 2004,
pp. 881-892.
[11] A. Sanin, C. Sanderson, B.C. Lovell. "Shadow Detection: A Survey and
Comparative Evaluation of Recent Methods," Pattern Recognition, Vol.
45, No. 4, 2012, pp. 1684-1695.
[12] F. Kristensen, P. Nilsson, and V. Öwall, "Background segmentation
beyond RGB," in Proc. Asian Conf. Computer Vision, vol. 2, 2006, pp.
602-612.
@article{"International Journal of Information, Control and Computer Sciences:53150", author = "Peng Shen Ong and Yoong Choon Chang and Chee Pun Ooi and Ettikan K. Karuppiah and Shahirina Mohd Tahir", title = "An FPGA Implementation of Intelligent Visual Based Fall Detection", abstract = "Falling has been one of the major concerns and threats
to the independence of the elderly in their daily lives. With the
worldwide significant growth of the aging population, it is essential
to have a promising solution of fall detection which is able to operate
at high accuracy in real-time and supports large scale implementation
using multiple cameras. Field Programmable Gate Array (FPGA) is a
highly promising tool to be used as a hardware accelerator in many
emerging embedded vision based system. Thus, it is the main
objective of this paper to present an FPGA-based solution of visual
based fall detection to meet stringent real-time requirements with
high accuracy. The hardware architecture of visual based fall
detection which utilizes the pixel locality to reduce memory accesses
is proposed. By exploiting the parallel and pipeline architecture of
FPGA, our hardware implementation of visual based fall detection
using FGPA is able to achieve a performance of 60fps for a series of
video analytical functions at VGA resolutions (640x480). The results
of this work show that FPGA has great potentials and impacts in
enabling large scale vision system in the future healthcare industry
due to its flexibility and scalability.", keywords = "Fall detection, FPGA, hardware implementation.", volume = "7", number = "2", pages = "193-6", }