Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31198
Toward Indoor and Outdoor Surveillance Using an Improved Fast Background Subtraction Algorithm

Authors: A. El Harraj, N. Raissouni


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, Object Detection, object tracking, background subtraction, contrast limited histogram equalization, illumination invariance, behavior understanding, dynamic scenes

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1768


[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.