Background subtraction with outlier replacement

A data driven background subtraction algorithm where each background pixel is modeled with a representative set of samples is presented. The samples are pixel values observed in preceding frames. Each pixel in an incoming frame is classified as background or foreground by comparing the pixel value with the samples in pixel's background model. The background model is periodically updated by replacing the most outlying sample in the model with the current pixel value. Techniques for suppressing dynamic background and ghost regions are incorporated in the algorithm. Evaluation tests on a public dataset shows markedly faster ghost suppression and fewer false positives in dynamic background regions, as well as improved overall performance in terms of evaluation metrics, compared to the base method.

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

[2]  Fatih Murat Porikli,et al.  A Novel Video Dataset for Change Detection Benchmarking , 2014, IEEE Transactions on Image Processing.

[3]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[4]  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.

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

[6]  Gerhard Rigoll,et al.  Background segmentation with feedback: The Pixel-Based Adaptive Segmenter , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[7]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Other Conferences.

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

[9]  D. Koller,et al.  Towards robust automatic traffic scene analysis in real-time , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

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

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

[12]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[13]  I. Haritaoglu,et al.  Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002 .

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

[15]  Thierry Bouwmans,et al.  Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey , 2008 .

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