Detecting changes in multiple images of the same
scene has recently seen increased interest due to the many
contemporary applications including smart security systems, smart
homes, remote sensing, surveillance, medical diagnosis, weather
forecasting, speed and distance measurement, post-disaster forensics
and much more. These applications differ in the scale, nature, and
speed of change. This paper presents an application of image
processing techniques to implement a real-time change detection
system. Change is identified by comparing the RGB representation of
two consecutive frames captured in real-time. The detection threshold
can be controlled to account for various luminance levels. The
comparison result is passed through a filter before decision making to
reduce false positives, especially at lower luminance conditions. The
system is implemented with a MATLAB Graphical User interface
with several controls to manage its operation and performance.
[1] RI Capó Irizarry, "Fundamentals & Applications of Image Change
Detection," Research Thrust R2 Presentations. Northeastern
University, Paper 20, 2006.
[2] Richard J. Radke et al, "Image Change Detection Algorithms: A
Systematic Survey," IEEE Transactions on Image Processing, Volume
14 , Issue 3, pp.294 – 307, 2005.
[3] P.L.Rosin, E.Ioannidis, "Evaluation of global image thresholding for
change detection," Pattern Recognition Lett. Vol. 24, Issue 14, pp
2345–2356, 2003.
[4] T. A. Pawar, "Change Detection Approach for Images Using Image
Fusion and C-means Clustering Algorithm," 4th International Journal
of Advance Research in Computer Science and Management Studies,
Volume 2, Issue 10, pp.303-307, 2014.
[5] M. Ilsever, C. Unsalan, "Pixel-Based Change Detection Methods,
Remote Sensing Aplications," Vol X, pp 7-21, 2012, Springer.
[6] Ashok Sundaresan, et al., "Robustness of Change Detection Algorithms
in the Presence of Registration Errors," Photogrammetric Engineering
& Remote Sensing, Volume 74, Issue 4, pp.375-383, April 2007
[7] S. Minu, et al., "A Comparative Study of Image Change Detection
Algorithms in MATLAB," Aquatic Procedia , Vol 4, pp 1366–1373,
2015.
[1] RI Capó Irizarry, "Fundamentals & Applications of Image Change
Detection," Research Thrust R2 Presentations. Northeastern
University, Paper 20, 2006.
[2] Richard J. Radke et al, "Image Change Detection Algorithms: A
Systematic Survey," IEEE Transactions on Image Processing, Volume
14 , Issue 3, pp.294 – 307, 2005.
[3] P.L.Rosin, E.Ioannidis, "Evaluation of global image thresholding for
change detection," Pattern Recognition Lett. Vol. 24, Issue 14, pp
2345–2356, 2003.
[4] T. A. Pawar, "Change Detection Approach for Images Using Image
Fusion and C-means Clustering Algorithm," 4th International Journal
of Advance Research in Computer Science and Management Studies,
Volume 2, Issue 10, pp.303-307, 2014.
[5] M. Ilsever, C. Unsalan, "Pixel-Based Change Detection Methods,
Remote Sensing Aplications," Vol X, pp 7-21, 2012, Springer.
[6] Ashok Sundaresan, et al., "Robustness of Change Detection Algorithms
in the Presence of Registration Errors," Photogrammetric Engineering
& Remote Sensing, Volume 74, Issue 4, pp.375-383, April 2007
[7] S. Minu, et al., "A Comparative Study of Image Change Detection
Algorithms in MATLAB," Aquatic Procedia , Vol 4, pp 1366–1373,
2015.
@article{"International Journal of Information, Control and Computer Sciences:71314", author = "Madina Hamiane and Amina Khunji", title = "A Real-Time Image Change Detection System", abstract = "Detecting changes in multiple images of the same
scene has recently seen increased interest due to the many
contemporary applications including smart security systems, smart
homes, remote sensing, surveillance, medical diagnosis, weather
forecasting, speed and distance measurement, post-disaster forensics
and much more. These applications differ in the scale, nature, and
speed of change. This paper presents an application of image
processing techniques to implement a real-time change detection
system. Change is identified by comparing the RGB representation of
two consecutive frames captured in real-time. The detection threshold
can be controlled to account for various luminance levels. The
comparison result is passed through a filter before decision making to
reduce false positives, especially at lower luminance conditions. The
system is implemented with a MATLAB Graphical User interface
with several controls to manage its operation and performance.", keywords = "Image change detection, Image processing, image
filtering, thresholding, B/W quantization.", volume = "9", number = "9", pages = "2106-4", }