The aim of this research is to develop a fast and
reliable surveillance system based on a personal digital assistant
(PDA) device. This is to extend the capability of the device to detect
moving objects which is already available in personal computers.
Secondly, to compare the performance between Background
subtraction (BS) and Temporal Frame Differencing (TFD) techniques
for PDA platform as to which is more suitable. In order to reduce
noise and to prepare frames for the moving object detection part,
each frame is first converted to a gray-scale representation and then
smoothed using a Gaussian low pass filter. Two moving object
detection schemes i.e., BS and TFD have been analyzed. The
background frame is updated by using Infinite Impulse Response
(IIR) filter so that the background frame is adapted to the varying
illuminate conditions and geometry settings. In order to reduce the
effect of noise pixels resulting from frame differencing
morphological filters erosion and dilation are applied. In this
research, it has been found that TFD technique is more suitable for
motion detection purpose than the BS in term of speed. On average
TFD is approximately 170 ms faster than the BS technique
[1] Z. Luo and C.H. Wu, "A Chinese Character Recognition Interface for
Mobile Communication Devices Using Fuzzy Logic and Unit
Extraction", Proceedings of the IEEE IECON 22nd International
Conference on Industrial Electronics, Control, and Instrumentation,
August 1996. 572-577
[2] H. Kang and H.J. Kim, "Design of an Interface on PDA for Korean",
IEEE Transactions on Consumer Electronics, Vol.46, Issue: 3, pp.834-
838, August 2000.
[3] Yu-Fei Ma, Hong-Jiang Zhang. Detecting Motion Object By Spatio-
Temporal Entropy Microsoft Research, China 5F, Sigma Center, 49 Zhi
Chun Road Beijing, China.
[4] J. Breitbart, D. Balakrishnan, and A. Ganz, "Pocket-IMPACT Software
for Delivering Online Courseware on a PDA: Challenges, Design
Guidelines and Implementation", Proceedings of IEEE Frontiers in
Education Conference, Vol.1, pp. T3F-5, November 2002.
[5] X. Vila, A. Riera, E. Sanchez, M. Lama, D. L. Mureno, "A PDA-based
Interface for a Computer Supported Educational System", Proceedings
of the 3rd IEEE International Conference on Advanced Learning
Technologies, pp.12-16, July 2003.
[6] J. Yang, X. Chen, and W. Kunz, "A PDA-based Face Recognition
System", Proceedings of the Sixth IEEE Workshop on Applications of
Computer Vision, pp. 19-23, December 2002.
[7] Sheng-Tun Li, Huang-Chih Hsieh, Ly-Yen Shue, Wen-Shen Chen "PDA
Watch for Mobile Surveillance Services". Department of Information
Management National Kaohsiung First University of Science
Technology 2 Juoyue Rd., Nantz District, Kaohsiung 811, Taiwan.
[8] Gregory A. Baxes. 1996. |Digital Image Processing: Principles and
Applications. New York: John willey & Sons.
[9] A. J. Lipton, H. Fujiyoshi, and R.S. Patil. Moving target classification
and tracking from real-time video. In Proc. of Workshop Applications of
Computer Vision, pages 129-136, 1998.
[10] Jeff Prosise. Programming Windows with MFC. Redmond, Washington:
Microsoft Press. 1999.
[11] Richardh.Carverkuo Chungtai. Modern Multithreading. Hoboken, New
Jersey .John Wiley & Sons. 2006.
[1] Z. Luo and C.H. Wu, "A Chinese Character Recognition Interface for
Mobile Communication Devices Using Fuzzy Logic and Unit
Extraction", Proceedings of the IEEE IECON 22nd International
Conference on Industrial Electronics, Control, and Instrumentation,
August 1996. 572-577
[2] H. Kang and H.J. Kim, "Design of an Interface on PDA for Korean",
IEEE Transactions on Consumer Electronics, Vol.46, Issue: 3, pp.834-
838, August 2000.
[3] Yu-Fei Ma, Hong-Jiang Zhang. Detecting Motion Object By Spatio-
Temporal Entropy Microsoft Research, China 5F, Sigma Center, 49 Zhi
Chun Road Beijing, China.
[4] J. Breitbart, D. Balakrishnan, and A. Ganz, "Pocket-IMPACT Software
for Delivering Online Courseware on a PDA: Challenges, Design
Guidelines and Implementation", Proceedings of IEEE Frontiers in
Education Conference, Vol.1, pp. T3F-5, November 2002.
[5] X. Vila, A. Riera, E. Sanchez, M. Lama, D. L. Mureno, "A PDA-based
Interface for a Computer Supported Educational System", Proceedings
of the 3rd IEEE International Conference on Advanced Learning
Technologies, pp.12-16, July 2003.
[6] J. Yang, X. Chen, and W. Kunz, "A PDA-based Face Recognition
System", Proceedings of the Sixth IEEE Workshop on Applications of
Computer Vision, pp. 19-23, December 2002.
[7] Sheng-Tun Li, Huang-Chih Hsieh, Ly-Yen Shue, Wen-Shen Chen "PDA
Watch for Mobile Surveillance Services". Department of Information
Management National Kaohsiung First University of Science
Technology 2 Juoyue Rd., Nantz District, Kaohsiung 811, Taiwan.
[8] Gregory A. Baxes. 1996. |Digital Image Processing: Principles and
Applications. New York: John willey & Sons.
[9] A. J. Lipton, H. Fujiyoshi, and R.S. Patil. Moving target classification
and tracking from real-time video. In Proc. of Workshop Applications of
Computer Vision, pages 129-136, 1998.
[10] Jeff Prosise. Programming Windows with MFC. Redmond, Washington:
Microsoft Press. 1999.
[11] Richardh.Carverkuo Chungtai. Modern Multithreading. Hoboken, New
Jersey .John Wiley & Sons. 2006.
@article{"International Journal of Electrical, Electronic and Communication Sciences:63057", author = "Basem Mustafa Abd. Amer and Syed Abdul Rahman Al-Attas", title = "Smart Surveillance using PDA", abstract = "The aim of this research is to develop a fast and
reliable surveillance system based on a personal digital assistant
(PDA) device. This is to extend the capability of the device to detect
moving objects which is already available in personal computers.
Secondly, to compare the performance between Background
subtraction (BS) and Temporal Frame Differencing (TFD) techniques
for PDA platform as to which is more suitable. In order to reduce
noise and to prepare frames for the moving object detection part,
each frame is first converted to a gray-scale representation and then
smoothed using a Gaussian low pass filter. Two moving object
detection schemes i.e., BS and TFD have been analyzed. The
background frame is updated by using Infinite Impulse Response
(IIR) filter so that the background frame is adapted to the varying
illuminate conditions and geometry settings. In order to reduce the
effect of noise pixels resulting from frame differencing
morphological filters erosion and dilation are applied. In this
research, it has been found that TFD technique is more suitable for
motion detection purpose than the BS in term of speed. On average
TFD is approximately 170 ms faster than the BS technique", keywords = "Surveillance, PDA, Motion Detection, ImageProcessing , Background Subtraction.", volume = "4", number = "6", pages = "997-5", }