Foreground model for background subtraction with blind updating

Background subtraction is a key pre-processing step for several video automatic operations. Various techniques have been proposed to perform background subtraction automatically in complex environments. Visual background extractor (ViBe) is a popular background subtraction technique that can initialize its model in a single frame, adapt to the environment changes and achieve satisfactory subtraction results. In this paper, we propose to use ViBe with blind updating which can more quickly adapt to dynamic environment changes. We propose foreground model with adaptive updating strategy to assist the ViBe with blind updating to detect slow moving object without introducing the ghost phenomenon. Experimental results have verified the performance of the proposed technique.

[1]  Marc Van Droogenbroeck,et al.  Background subtraction: Experiments and improvements for ViBe , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

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

[3]  Marc Van Droogenbroeck,et al.  ViBE: A powerful random technique to estimate the background in video sequences , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

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

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

[6]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  David Suter,et al.  Background Subtraction Based on a Robust Consensus Method , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[8]  David Suter,et al.  A consensus-based method for tracking: Modelling background scenario and foreground appearance , 2007, Pattern Recognit..

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

[10]  Jen-Hui Chuang,et al.  Regularized Background Adaptation: A Novel Learning Rate Control Scheme for Gaussian Mixture Modeling , 2011, IEEE Transactions on Image Processing.

[11]  Marko Heikkilä,et al.  A texture-based method for modeling the background and detecting moving objects , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Larry S. Davis,et al.  Real-time foreground-background segmentation using codebook model , 2005, Real Time Imaging.

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

[14]  Max Mignotte,et al.  Statistical background subtraction using spatial cues , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

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

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

[17]  Luo Di,et al.  Texture Analysis for Shadow Removing and Tracking of Vehicle in Traffic Monitoring System , 2008, 2008 International Symposium on Intelligent Information Technology Application Workshops.

[18]  Ferdinand van der Heijden,et al.  Recursive unsupervised learning of finite mixture models , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.