Background subtraction for static & moving camera

Background subtraction is one of the most commonly used components in machine vision systems. Despite the numerous algorithms proposed in the literature and used in practical applications, key challenges remain in designing a single system that can handle diverse environmental conditions. In this paper we present Multiple Background Model based Background Subtraction Algorithm as such a candidate. The algorithm was originally designed for handling sudden illumination changes. The new version has been refined with changes at different steps of the process, specifically in terms of selecting optimal color space, clustering of training images for Background Model Bank and parameter for each channel of color space. This has allowed the algorithm's applicability to wide variety of challenges associated with change detection including camera jitter, dynamic background, Intermittent Object Motion, shadows, bad weather, thermal, night videos etc. Comprehensive evaluation demonstrates the superiority of algorithm against state of the art.

[1]  Fatih Murat Porikli,et al.  CDnet 2014: An Expanded Change Detection Benchmark Dataset , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[2]  Guillaume-Alexandre Bilodeau,et al.  SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity , 2015, IEEE Transactions on Image Processing.

[3]  Pascal Fua,et al.  Making Background Subtraction Robust to Sudden Illumination Changes , 2008, ECCV.

[4]  Guillaume-Alexandre Bilodeau,et al.  A Multiscale Region-Based Motion Detection and Background Subtraction Algorithm , 2010, Sensors.

[5]  Zezhi Chen,et al.  Background Subtraction in Video Using Recursive Mixture Models, Spatio-Temporal Filtering and Shadow Removal , 2009, ISVC.

[6]  Benjamin Höferlin,et al.  Evaluation of background subtraction techniques for video surveillance , 2011, CVPR 2011.

[7]  Hasan Sajid,et al.  Background subtraction under sudden illumination change , 2014, 2014 IEEE 16th International Workshop on Multimedia Signal Processing (MMSP).

[8]  Rui Wang,et al.  Static and Moving Object Detection Using Flux Tensor with Split Gaussian Models , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[9]  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).

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

[11]  Jonathan H. Connell,et al.  A Statistical Approach for Real-time Robust Background Subtrac tion and Shadow Detection , 2014 .

[12]  Massimo De Gregorio,et al.  Change Detection with Weightless Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[13]  Joost van de Weijer,et al.  Color in Computer Vision: Fundamentals and Applications , 2012 .

[14]  Lucia Maddalena,et al.  A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection , 2010, Neural Computing and Applications.

[15]  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..

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

[17]  Chandrika Kamath,et al.  Robust techniques for background subtraction in urban traffic video , 2004, IS&T/SPIE Electronic Imaging.

[18]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interaction , 1999, ICVS.

[19]  Hélène Laurent,et al.  Comparative study of background subtraction algorithms , 2010, J. Electronic Imaging.

[20]  Bin Wang,et al.  A Fast Self-Tuning Background Subtraction Algorithm , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[21]  Xiqun Lu,et al.  A multiscale spatio-temporal background model for motion detection , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[22]  Kazuhiko Sumi,et al.  Background subtraction based on cooccurrence of image variations , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

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

[24]  Lucia Maddalena,et al.  The SOBS algorithm: What are the limits? , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

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

[26]  Mansour Moniri,et al.  Spectral-360: A Physics-Based Technique for Change Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[27]  Marc Van Droogenbroeck,et al.  ViBe: A Universal Background Subtraction Algorithm for Video Sequences , 2011, IEEE Transactions on Image Processing.

[28]  Caifeng Shan,et al.  Background Subtraction under Sudden Illumination Changes , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[29]  Huiyu Zhou,et al.  Spatial mixture of Gaussians for dynamic background modelling , 2013, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance.