Abstract
The presence of dynamic scene is a challenging problem in video surveillance systems tasks. Mixture of Gaussian (MOG) is the most appropriate method to model dynamic background. However, local variations and the instant variations in the brightness decrease the performance of the later. We present in this paper a novel and efficient method that will significantly reduce MOG drawbacks by an improved parameters updating algorithm. Starting from a normalization step, we divide each extracted frame into several blocks. Then, we apply an improved updating algorithm for each block to control local variation. When a significant environment changes are detected in one or more blocs, the parameters of MOG assigned to these blocks are updated and the parameters of the rest remain the same. Experimental results demonstrate that the proposed approach is effective and efficient compared with state-of-the-art background subtraction methods.
Chapter PDF
Similar content being viewed by others
References
Baf, F.E., Bouwmans, T., Vachon, B.: Fuzzy statistical modeling of dynamic backgrounds for moving object detection in infrared videos. In: Computer Vision and Pattern Recognition (2009)
Caseiro, R., Henriques, J.F., Batista, J.: Foreground Segmentation via Background Modeling on Riemannian Manifolds. In: International Conference on Pattern Recognition, pp. 3570–3574 (2010)
Charoenpong, T., Supasuteekul, A., Nuthong, C.: Adaptive background modeling from an image sequence by using K-Means clustering (2010)
Cheng, F.C., Huang, S.C., Ruan, S.J.: Illumination-Sensitive Background Modeling Approach for Accurate Moving Object Detection. IEEE Transactions on Broadcasting 57, 794–801 (2011)
Cristani, M., Bicegi, M., Murino, V.: Integrated Region-and Pixel-based Approach to Background Modeling (2002)
Cristani, M., Murino, V.: A spatial sampling mechanism for effective background subtraction. In: Computer Vision Theory and Applications, pp. 403–412 (2007)
Cristani, M., Murino, V.: Background Subtraction with Adaptive Spatio-Temporal Neighborhood Analysis. In: Computer Vision Theory and Applications, pp. 484–489 (2008)
Djouadi, A., Snorrason, G.F.D.: The Quality of Training Sample Estimates of the Bhattacharyya Coefficient. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 92–97 (1990)
Doulamis, A., Kalisperakis, I., Stentoumis, C., Matsatsinis, N.: Self Adaptive background modeling for identifying persons’ falls. In: International Workshop on Semantic Media Adaptation and Personalization (2010)
Elgammal, A.M., Harwood, D., Davis, L.S.: Non-parametric Model for Background Subtraction (2000)
Friedman, N., Russell, S.J.: Image Segmentation in Video Sequences: A Probabilistic Approach. In: Uncertainty in Artificial Intelligence, pp. 175–181 (1997)
Hayman, E., Olof Eklundh, J.: Statistical Background Subtraction for a Mobile Observer. In: International Conference on Computer Vision, pp. 67–74 (2003)
Hedayati, M., Zaki, W.M.D.W., Hussain, A.: Real-time background subtraction for video surveillance: From research to reality. In: International Colloquium on Signal Processing & Its Applications (2010)
Kaewtrakulpong, P., Bowden, R.: An improved adaptive background mixture model for real time tracking with shadow dectection (2001)
Kan, J., Li, K., Tang, J., Du, X.: Background modeling method based on improved multi-Gaussian distribution. In: International Conference on Computer Application and System Modeling (2010)
Li, L., Huang, W., Gu, I.Y.H., Tian, Q.: Statistical modeling of complex backgrounds for foreground object detection. IEEE Transactions on Image Processing 13, 1459–1472 (2004)
Li, X., Jing, X.: FPGA based mixture Gaussian background modeling and motion detection 4, 2078–2081 (2011)
Martel-brisson, N., Zaccarin, A.: Learning and Removing Cast Shadows through a Multidistribution Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1133–1146 (2007)
Oliver, N.M., Rosario, B., Pentland, A.P.: A Bayesian Computer Vision System for Modeling Human Interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 831–843 (2000)
Power, P.W., Schoonees, J.A.: Understanding Background Mixture Models for Foreground Segmentation (2002)
Prati, A.: c, I.M., Trivedi, M.M., Cucchiara, R.: Detecting Moving Shadows: Formulation, Algorithms and Evaluation
Schindler, K., Wang, H.: Smooth Foreground-Background Segmentation for Video Processing (2006)
Seki, M., Okuda, H., Hashimoto, M., Hirata, N.: Object modeling using gaussian mixture model for infrared image and its application to vehicle detection. Journal of Robotics and Mechatronics 18(6), 738 (2006)
Setiawan, N.A., Ju Hong, S., Woon Kim, J., Woo Lee, C.: Gaussian Mixture Model in Improved HLS Color Space for Human Silhouette Extraction (2006)
Sheng, Z.B., Cui, X.Y.: An adaptive learning rate GMM for background extraction. Optoelectronics Letters 4, 460–463 (2008)
Stauffer, C., Grimson, W.E.L.: Adaptive Background Mixture Models for Real-Time Tracking. Computer Vision and Pattern Recognition 2, 2246–2252 (1999)
Stenger, B., Ramesh, V., Paragios, N., Coetzee, F., Buhmann, J.M.: Topology Free Hidden Markov Models: Application to Background Modeling. In: International Conference on Computer Vision, pp. 294–301 (2001)
Suo, P., Wang, Y.: An improved adaptive background modeling algorithm based on Gaussian Mixture Model. In: International Conference on Signal Processing Proceedings (2008)
Wang, H., Suter, D.: A re-evaluation of mixture of Gaussian background modeling [video signal processing applications]. In: International Conference on Acoustics, Speech, and Signal Processing, vol. 2 (2005)
Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-Time Tracking of the Human Body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 780–785 (1997)
Yu, J., Zhou, X., Qian, F.: Object kinematic model: A novel approach of adaptive background mixture models for video segmentation (2010)
Zang, Q., Klette, R.: Evaluation of an Adaptive Composite Gaussian Model in Video Surveillance (2003)
Zhang, L., Liang, Y.: Motion Human Detection Based on Background Subtraction. In: International Workshop on Education Technology and Computer Science (2010)
Zivkovic, Z., Heijden, F.V.D.: Recursive Unsupervised Learning of Finite Mixture Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 651–656 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 IFIP International Federation for Information Processing
About this paper
Cite this paper
Farou, B., Seridi, H., Akdag, H. (2015). Improved Parameters Updating Algorithm for the Detection of Moving Objects. In: Amine, A., Bellatreche, L., Elberrichi, Z., Neuhold, E., Wrembel, R. (eds) Computer Science and Its Applications. CIIA 2015. IFIP Advances in Information and Communication Technology, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-19578-0_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-19578-0_43
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19577-3
Online ISBN: 978-3-319-19578-0
eBook Packages: Computer ScienceComputer Science (R0)