Toward Indoor and Outdoor Surveillance Using an Improved Fast Background Subtraction Algorithm
The detection of moving objects from a video image
sequences is very important for object tracking, activity recognition,
and behavior understanding in video surveillance.
The most used approach for moving objects detection / tracking is
background subtraction algorithms. Many approaches have been
suggested for background subtraction. But, these are illumination
change sensitive and the solutions proposed to bypass this problem
are time consuming.
In this paper, we propose a robust yet computationally efficient
background subtraction approach and, mainly, focus on the ability to
detect moving objects on dynamic scenes, for possible applications in
complex and restricted access areas monitoring, where moving and
motionless persons must be reliably detected. It consists of three
main phases, establishing illumination changes invariance,
background/foreground modeling and morphological analysis for
noise removing.
We handle illumination changes using Contrast Limited Histogram
Equalization (CLAHE), which limits the intensity of each pixel to
user determined maximum. Thus, it mitigates the degradation due to
scene illumination changes and improves the visibility of the video
signal. Initially, the background and foreground images are extracted
from the video sequence. Then, the background and foreground
images are separately enhanced by applying CLAHE.
In order to form multi-modal backgrounds we model each channel
of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture
Model (GMM). Finally, we post process the resulting binary
foreground mask using morphological erosion and dilation
transformations to remove possible noise.
For experimental test, we used a standard dataset to challenge the
efficiency and accuracy of the proposed method on a diverse set of
dynamic scenes.
[1] T. Aach and A. Kaup. “Bayesian algorithms for adaptive change
detection in image sequences using markov random fields”. Signal
Processing: Image Communication, 7:147–160, 1995.
[2] V. Cheng and N. Kehtarnavaz. “A smart camera application: Dsp-based
people detection and tracking”. Journal of Electronic Imaging, 9(3):336–
346, 2000.
[3] S.C.S. Cheung and C. Kamath. “Robust techniques for background
subtraction in urban traffic video”. Visual Communications and Image
Processing, 5308:881–892, 2004.
[4] S.C.S. Cheung and C. Kamath. “Robust background subtraction with
foreground validation for urban traffic video”. EURASIP Journal on
Applied Signal Processing, 14:2330–2340, 2005.
[5] R. Cucchiara, C. Grana, M. Piccardi, and A. Prati. “Detecting moving
objects, ghosts and shadows in video streams”. Transactions on Pattern
Analysis and Machine Intelligence, pages 1337–1342, 2003.
[6] A Elgammal, D. Harwood, and L. Davis. “Non-parametric model for
background subtraction”. European Conference on Computer Vision,
pages 751–767, 2000.
[7] I. Haritaoglu, D. Harwood, and L.S. Davis. W 4: “real-time surveillance
of people and their activities”. Pattern Analysis and Machine
Intelligence, 22:809–830, 2000.
[8] S. Herrero and J. Bescos. “Background subtraction techniques:
systematic evaluation and comparative analysis”. International
conference on Advanced Concepts for Intelligent Vision Systems, pages
33–42, 2009.
[9] P. Kaew TraKul Pong and R. Bowden. “An improved adaptive
background mixture model for real-time tracking with shadow
detection”. Workshop on Advanced Video-based Surveillance Systems
conference, 2001.
[10] Zimmerman, JB, SM Pizer, EV Staab, JR Perry, W McCartney, BC
Brenton, “An Evaluation of the Effectiveness of Adaptive Histogram
Equalization for Contrast Enhancement”, IEEE Trans. Med. Imaging,
7(4): 304-312, 1988.
[11] C. Stauffer and W. E. L. Grimson. “Adaptive background mixture
models for real-time tracking”. IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, 1999, 2: 252, 1999.
[12] L. Vincent, “Morphological Grayscale reconstruction in image analysis:
applications and efficient algorithms,” IEEE Transactions On Image
Processing, vol. 2, no. 2, pp. 176−201, April 1993.
[13] Piccardi, Massimo. “Background subtraction techniques: a review”.
Systems, man and cybernetics, 2004 IEEE international conference on.
Vol. 4. IEEE, 2004.
[14] Gonzalez, Rafael C., Richard E. Woods, and Steven L. Eddins. Digital
image processing using MATLAB. Vol. 2. Knoxville: Gatesmark
Publishing, 2009.
[15] Horprasert, Thanarat, David Harwood, and Larry S. Davis. "A statistical
approach for real-time robust background subtraction and shadow
detection."IEEE ICCV. Vol. 99. 1999”.
[16] M. Harville. “A framework of high-level feedback to adaptive, per-pixel,
mixture of gaussian background models”. In Proceedings of the
European Conference on Computer Vision, 2002.
[17] R. Jain and H. Nagel. “On the analysis of accumulative difference
pictures from image sequences of real world scenes”. In IEEE
Transactions on Pattern Analysis and Machine Intelligence, 1979.
[18] N. Oliver, B. Rosario, and A. Pentland. “A bayesian computer vision
system for modeling human interactions”. In IEEE Transactions on
Pattern Analysis and Machine Intelligence, 2000.
[19] C. Wren, A. Azarbayejani, T. Darrel, and A. Pentland. Pfinder: “Real
time tracking of the human body”. In IEEE Transactions on Pattern
Analysis and Machine Intelligence, 1997.
[20] Yaser Ajmal Sheikh, Mubarak Shah, “Bayesian Modelling of Dynamic
Scenes for Object Detection”, IEEE Transactions on Pattern Analysis
and Machine Vision, 2005
[21] K.-P. Karmann, A. Brandt, and R. Gerl. “Using adaptive tracking to
classify and moitor activities in a site. In Time Varying Image
Processing and Moving Object Recognition”. Elsevier Science
Publishers, 1990.
[22] D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S.
Russell. “Towards robust automatic traffic scene analysis in real-time”.
In International Conference of Pattern Recognition, 1994.
[23] I. Haritaoglu, D. Harwood, and L. Davis. W4: “Real-time of people and
their activities”. In IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2000.
[24] N. Friedman and S. Russell. Image segmentation in video sequences: A
probabilistic approach. In Proceedings of the Thirteenth Conference on
Uncertainty in Artificial Intelligence, 1997.
[25] O. Javed, K. Shafique, and M. Shah. A hierarchical appraoch to robust
background subtraction using color and gradient information. In IEEE
Workshop on Motion and Video Computing, 2002.
[26] R. Pless, J. Larson, S. Siebers, and B. Westover. “Evaluation of local
models of dynamic backgrounds”. In IEEE Proceedings on Computer
Vision and Pattern Recognition, 2003.
[27] A. Mittal and N. Paragios. Motion-based background subtraction using
adaptive kernel density estimation. In IEEE Proceedings on Computer
Vision and Pattern Recognition, 2004.
[28] Suprijanto, Gianto, E. Juliastuti, Azhari, and Lusi Epsilawati, "Image
Contrast Enhancement for Film-Based Dental Panoramic Radiography,"
in International Conference on System Engineering and Technology,
Bandung, Indonesia, 2012.
[29] J. van deWeijer, T. Gevers, and A. Bagdanov, “ Boosting color saliency
in image feature detection”, IEEE Trans. Pattern Analysis and Machine
Intell., 28(1):150–156, 2006.
[30] A. Godbehere, A. Matsukawa and K. Goldberg. Visual Tracking of
Human Visitors under Variable-Lighting Conditions for a Responsive
Audio Art Installation. American Control Conference, Montreal, June
2012.
[31] J. Gil and R. Kimmel, “Efficient dilation, erosion, opening, and closing
algorithms”, IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 24, no. 12, pp. 1606−1617, December 2002.
[32] http://www.cs.cmu.edu/~yaser/new_backgroundsubtraction.htm
[33] http://www.inventive-technologies.com/
[34] http://www.creargie.com/
[1] T. Aach and A. Kaup. “Bayesian algorithms for adaptive change
detection in image sequences using markov random fields”. Signal
Processing: Image Communication, 7:147–160, 1995.
[2] V. Cheng and N. Kehtarnavaz. “A smart camera application: Dsp-based
people detection and tracking”. Journal of Electronic Imaging, 9(3):336–
346, 2000.
[3] S.C.S. Cheung and C. Kamath. “Robust techniques for background
subtraction in urban traffic video”. Visual Communications and Image
Processing, 5308:881–892, 2004.
[4] S.C.S. Cheung and C. Kamath. “Robust background subtraction with
foreground validation for urban traffic video”. EURASIP Journal on
Applied Signal Processing, 14:2330–2340, 2005.
[5] R. Cucchiara, C. Grana, M. Piccardi, and A. Prati. “Detecting moving
objects, ghosts and shadows in video streams”. Transactions on Pattern
Analysis and Machine Intelligence, pages 1337–1342, 2003.
[6] A Elgammal, D. Harwood, and L. Davis. “Non-parametric model for
background subtraction”. European Conference on Computer Vision,
pages 751–767, 2000.
[7] I. Haritaoglu, D. Harwood, and L.S. Davis. W 4: “real-time surveillance
of people and their activities”. Pattern Analysis and Machine
Intelligence, 22:809–830, 2000.
[8] S. Herrero and J. Bescos. “Background subtraction techniques:
systematic evaluation and comparative analysis”. International
conference on Advanced Concepts for Intelligent Vision Systems, pages
33–42, 2009.
[9] P. Kaew TraKul Pong and R. Bowden. “An improved adaptive
background mixture model for real-time tracking with shadow
detection”. Workshop on Advanced Video-based Surveillance Systems
conference, 2001.
[10] Zimmerman, JB, SM Pizer, EV Staab, JR Perry, W McCartney, BC
Brenton, “An Evaluation of the Effectiveness of Adaptive Histogram
Equalization for Contrast Enhancement”, IEEE Trans. Med. Imaging,
7(4): 304-312, 1988.
[11] C. Stauffer and W. E. L. Grimson. “Adaptive background mixture
models for real-time tracking”. IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, 1999, 2: 252, 1999.
[12] L. Vincent, “Morphological Grayscale reconstruction in image analysis:
applications and efficient algorithms,” IEEE Transactions On Image
Processing, vol. 2, no. 2, pp. 176−201, April 1993.
[13] Piccardi, Massimo. “Background subtraction techniques: a review”.
Systems, man and cybernetics, 2004 IEEE international conference on.
Vol. 4. IEEE, 2004.
[14] Gonzalez, Rafael C., Richard E. Woods, and Steven L. Eddins. Digital
image processing using MATLAB. Vol. 2. Knoxville: Gatesmark
Publishing, 2009.
[15] Horprasert, Thanarat, David Harwood, and Larry S. Davis. "A statistical
approach for real-time robust background subtraction and shadow
detection."IEEE ICCV. Vol. 99. 1999”.
[16] M. Harville. “A framework of high-level feedback to adaptive, per-pixel,
mixture of gaussian background models”. In Proceedings of the
European Conference on Computer Vision, 2002.
[17] R. Jain and H. Nagel. “On the analysis of accumulative difference
pictures from image sequences of real world scenes”. In IEEE
Transactions on Pattern Analysis and Machine Intelligence, 1979.
[18] N. Oliver, B. Rosario, and A. Pentland. “A bayesian computer vision
system for modeling human interactions”. In IEEE Transactions on
Pattern Analysis and Machine Intelligence, 2000.
[19] C. Wren, A. Azarbayejani, T. Darrel, and A. Pentland. Pfinder: “Real
time tracking of the human body”. In IEEE Transactions on Pattern
Analysis and Machine Intelligence, 1997.
[20] Yaser Ajmal Sheikh, Mubarak Shah, “Bayesian Modelling of Dynamic
Scenes for Object Detection”, IEEE Transactions on Pattern Analysis
and Machine Vision, 2005
[21] K.-P. Karmann, A. Brandt, and R. Gerl. “Using adaptive tracking to
classify and moitor activities in a site. In Time Varying Image
Processing and Moving Object Recognition”. Elsevier Science
Publishers, 1990.
[22] D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S.
Russell. “Towards robust automatic traffic scene analysis in real-time”.
In International Conference of Pattern Recognition, 1994.
[23] I. Haritaoglu, D. Harwood, and L. Davis. W4: “Real-time of people and
their activities”. In IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2000.
[24] N. Friedman and S. Russell. Image segmentation in video sequences: A
probabilistic approach. In Proceedings of the Thirteenth Conference on
Uncertainty in Artificial Intelligence, 1997.
[25] O. Javed, K. Shafique, and M. Shah. A hierarchical appraoch to robust
background subtraction using color and gradient information. In IEEE
Workshop on Motion and Video Computing, 2002.
[26] R. Pless, J. Larson, S. Siebers, and B. Westover. “Evaluation of local
models of dynamic backgrounds”. In IEEE Proceedings on Computer
Vision and Pattern Recognition, 2003.
[27] A. Mittal and N. Paragios. Motion-based background subtraction using
adaptive kernel density estimation. In IEEE Proceedings on Computer
Vision and Pattern Recognition, 2004.
[28] Suprijanto, Gianto, E. Juliastuti, Azhari, and Lusi Epsilawati, "Image
Contrast Enhancement for Film-Based Dental Panoramic Radiography,"
in International Conference on System Engineering and Technology,
Bandung, Indonesia, 2012.
[29] J. van deWeijer, T. Gevers, and A. Bagdanov, “ Boosting color saliency
in image feature detection”, IEEE Trans. Pattern Analysis and Machine
Intell., 28(1):150–156, 2006.
[30] A. Godbehere, A. Matsukawa and K. Goldberg. Visual Tracking of
Human Visitors under Variable-Lighting Conditions for a Responsive
Audio Art Installation. American Control Conference, Montreal, June
2012.
[31] J. Gil and R. Kimmel, “Efficient dilation, erosion, opening, and closing
algorithms”, IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 24, no. 12, pp. 1606−1617, December 2002.
[32] http://www.cs.cmu.edu/~yaser/new_backgroundsubtraction.htm
[33] http://www.inventive-technologies.com/
[34] http://www.creargie.com/
@article{"International Journal of Information, Control and Computer Sciences:69474", author = "A. El Harraj and N. Raissouni", title = "Toward Indoor and Outdoor Surveillance Using an Improved Fast Background Subtraction Algorithm", abstract = "The detection of moving objects from a video image
sequences is very important for object tracking, activity recognition,
and behavior understanding in video surveillance.
The most used approach for moving objects detection / tracking is
background subtraction algorithms. Many approaches have been
suggested for background subtraction. But, these are illumination
change sensitive and the solutions proposed to bypass this problem
are time consuming.
In this paper, we propose a robust yet computationally efficient
background subtraction approach and, mainly, focus on the ability to
detect moving objects on dynamic scenes, for possible applications in
complex and restricted access areas monitoring, where moving and
motionless persons must be reliably detected. It consists of three
main phases, establishing illumination changes invariance,
background/foreground modeling and morphological analysis for
noise removing.
We handle illumination changes using Contrast Limited Histogram
Equalization (CLAHE), which limits the intensity of each pixel to
user determined maximum. Thus, it mitigates the degradation due to
scene illumination changes and improves the visibility of the video
signal. Initially, the background and foreground images are extracted
from the video sequence. Then, the background and foreground
images are separately enhanced by applying CLAHE.
In order to form multi-modal backgrounds we model each channel
of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture
Model (GMM). Finally, we post process the resulting binary
foreground mask using morphological erosion and dilation
transformations to remove possible noise.
For experimental test, we used a standard dataset to challenge the
efficiency and accuracy of the proposed method on a diverse set of
dynamic scenes.
", keywords = "Video surveillance, background subtraction,
Contrast Limited Histogram Equalization, illumination invariance,
object tracking, object detection, behavior understanding, dynamic
scenes.", volume = "9", number = "4", pages = "922-6", }
