Video Foreground Detection Based on Symmetric Alpha-Stable Mixture Models

Background subtraction (BS) is an efficient technique for detecting moving objects in video sequences. A simple BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. These assumptions restrict the applicability of BS methods to real-time object detection in video. In this letter, we propose an extended cluster BS technique with a mixture of symmetric alpha-stable (SαS) distributions. An online self-adaptive mechanism is presented that allows automated estimation of the model parameters using the log moment method. Results over real video sequences from indoor and outdoor environments, with data from static and moving video cameras are presented. The SαS mixture model is shown to improve the detection performance compared with a cluster BS method using a Gaussian mixture model and the method of Li et al.

[1]  C. L. Nikias,et al.  Signal processing with fractional lower order moments: stable processes and their applications , 1993, Proc. IEEE.

[2]  Chrysostomos L. Nikias,et al.  Parameter estimation and blind channel identification in impulsive signal environments , 1995, IEEE Trans. Signal Process..

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

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

[5]  Guido Gerig,et al.  Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation , 2001, MICCAI.

[6]  Nikos Paragios,et al.  A MRF-based approach for real-time subway monitoring , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[7]  Joachim M. Buhmann,et al.  Topology Free Hidden Markov Models: Application to Background Modeling , 2001, ICCV.

[8]  Mubarak Shah,et al.  A hierarchical approach to robust background subtraction using color and gradient information , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[9]  Jan-Olof Eklundh,et al.  Statistical background subtraction for a mobile observer , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[10]  Rita Cucchiara,et al.  Detecting Moving Objects, Ghosts, and Shadows in Video Streams , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Chin-Seng Chua,et al.  Statistical background modeling for non-stationary camera , 2003, Pattern Recognit. Lett..

[12]  Ying Ming,et al.  Background Modeling and Subtraction Using a Local-linear-dependence-based Cauchy Statistical Model , 2003, DICTA.

[13]  Z. Zivkovic.  Improved adaptive Gaussian mixture model for background subtraction , 2004, ICPR 2004.

[14]  Qi Tian,et al.  Statistical modeling of complex backgrounds for foreground object detection , 2004, IEEE Transactions on Image Processing.

[15]  William B. Thompson,et al.  Detecting moving objects , 1989, International Journal of Computer Vision.

[16]  Zoran Zivkovic,et al.  Improved adaptive Gaussian mixture model for background subtraction , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[17]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[18]  Ferdinand van der Heijden,et al.  Efficient adaptive density estimation per image pixel for the task of background subtraction , 2006, Pattern Recognit. Lett..

[19]  David A. Forsyth,et al.  Tracking People by Learning Their Appearance , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Harish Bhaskar,et al.  Automatic Target Detection Based on Background Modeling Using Adaptive Cluster Density Estimation , 2007 .

[21]  Harish Bhaskar,et al.  Multi-resolution learning vector quantisation based automatic colour clustering , 2008, 2008 11th International Conference on Information Fusion.

[22]  Takeo Kanade,et al.  Background Subtraction for Freely Moving Cameras , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[23]  Diego P. Ruiz,et al.  Modelling with mixture of symmetric stable distributions using Gibbs sampling , 2010, Signal Process..