Background subtraction based on a Self-Adjusting MoG

The diversity in background scenes such as, illumination changes, dynamics of the background, camouflage effect, shadow, etc. is a big deal for moving objects detection methods makes it impossible to manage the multimodality of scenes in video surveillance systems. In this paper we present a new method that allows better detection of moving objects. This method combine the robustness of the Artificial Immune Recognition System (AIRS) with respect to the local variations and the power of Gaussian mixtures (GMM) to model changes at the pixel level. The task of the AIRS is to generate several GMM models for each pixel. This models are filtred through two mecanism: the competition for resources and the development of a candidate memory cell. The best model is merged with the exesting GMM according to the Memory cell introduction process. results obtained on the Wallflower dataset proved the performance of our system compared to other state-of-the-art methods.

[1]  P. KaewTrakulPong,et al.  An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection , 2002 .

[2]  Nebili Wafa,et al.  A New Process for Selecting the Best Background Representatives based on GMM , 2018 .

[3]  Du-Ming Tsai,et al.  Independent Component Analysis-Based Background Subtraction for Indoor Surveillance , 2009, IEEE Transactions on Image Processing.

[4]  Gerhard Rigoll,et al.  A deep convolutional neural network for video sequence background subtraction , 2018, Pattern Recognit..

[5]  Feng Qian,et al.  Object kinematic model: A novel approach of adaptive background mixture models for video segmentation , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[6]  P. Wayne Power,et al.  Understanding Background Mixture Models for Foreground Segmentation , 2002 .

[7]  Xiaoqin Zhang,et al.  Robust foreground segmentation based on two effective background models , 2008, MIR '08.

[8]  Joseph L. Mundy,et al.  Background Modeling Based on Subpixel Edges , 2007, 2007 IEEE International Conference on Image Processing.

[9]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[10]  Serhat Selcuk Bucak,et al.  Video Content Representation by Incremental Non-Negative Matrix Factorization , 2007, 2007 IEEE International Conference on Image Processing.

[11]  A. B. Watkins,et al.  A new classifier based on resource limited artificial immune systems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[12]  T. Charoenpong,et al.  Adaptive background modeling from an image sequence by using K-Means clustering , 2010, ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[13]  Howida A. Shedeed,et al.  A new technique for background modeling and subtraction for motion detection in real-time videos , 2010, 2010 IEEE International Conference on Image Processing.

[14]  Thierry Bouwmans,et al.  Fuzzy statistical modeling of dynamic backgrounds for moving object detection in infrared videos , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[15]  Long Ang Lim,et al.  Foreground Segmentation Using a Triplet Convolutional Neural Network for Multiscale Feature Encoding , 2018, Pattern Recognit. Lett..

[16]  Stuart J. Russell,et al.  Image Segmentation in Video Sequences: A Probabilistic Approach , 1997, UAI.

[17]  Takeo Kanade,et al.  A System for Video Surveillance and Monitoring , 2000 .

[18]  Konrad Schindler,et al.  Smooth Foreground-Background Segmentation for Video Processing , 2006, ACCV.

[19]  Iqbal Gondal,et al.  Automated multi-sensor color video fusion for nighttime video surveillance , 2010, The IEEE symposium on Computers and Communications.

[20]  Xu Jian,et al.  Background subtraction based on a combination of texture, color and intensity , 2008, 2008 9th International Conference on Signal Processing.

[21]  Mubarak Shah,et al.  Automatically Tuning Background Subtraction Parameters using Particle Swarm Optimization , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[22]  Viktor Öwall,et al.  Background Segmentation Beyond RGB , 2006, ACCV.

[23]  Herman Akdag,et al.  Efficient local monitoring approach for the task of background subtraction , 2017, Eng. Appl. Artif. Intell..

[24]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Zhiming Luo,et al.  Interactive deep learning method for segmenting moving objects , 2017, Pattern Recognit. Lett..

[26]  Hongxun zhang,et al.  Fusing Color and Texture Features for Background Model , 2006, FSKD.

[27]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[28]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Nikolaos F. Matsatsinis,et al.  Self Adaptive background modeling for identifying persons' falls , 2010, 2010 Fifth International Workshop Semantic Media Adaptation and Personalization.

[30]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Kentaro Toyama,et al.  Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[32]  Marc Van Droogenbroeck,et al.  Deep background subtraction with scene-specific convolutional neural networks , 2016, 2016 International Conference on Systems, Signals and Image Processing (IWSSIP).