Real-time background modeling based on a multi-level texture description

Background construction is the base of object detection and tracking of machine vision systems. Traditional background modeling methods often require complicated computations and are sensitive to illumination changes. This paper proposes a novel block-based background modeling method based on a hierarchical coarse-to-fine texture description, which fully utilizes the texture characteristics of each incoming frame. The proposed method is efficient and can resist both illumination changes and shadow disturbance. The experimental results show that this method is suitable for real-world scenes and real-time applications.

[1]  Zoran Duric,et al.  Using histograms to detect and track objects in color video , 2001, Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery.

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

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

[4]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[5]  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.

[6]  Walter Bender,et al.  Techniques for Data Hiding , 1996, IBM Syst. J..

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

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

[9]  Lucia Maddalena,et al.  A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications , 2008, IEEE Transactions on Image Processing.

[10]  L. Wixson.  Detecting Salient Motion by Accumulating Directionally-Consistent Flow , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Robert Sabourin,et al.  An adaptive classification system for video-based face recognition , 2012, Inf. Sci..

[12]  A. Enis Çetin,et al.  Silhouette-Based Method for Object Classification and Human Action Recognition in Video , 2006, ECCV Workshop on HCI.

[13]  Chun-Jen Chen,et al.  A linear-time component-labeling algorithm using contour tracing technique , 2004, Comput. Vis. Image Underst..

[14]  Wen-Hsiang Tsai,et al.  Data hiding in grayscale images by dynamic programming based on a human visual model , 2009, Pattern Recognit..

[15]  Fuchun Sun,et al.  Efficient visual tracking using particle filter with incremental likelihood calculation , 2012, Information Sciences.

[16]  Min Wu,et al.  Data hiding in image and video .I. Fundamental issues and solutions , 2003, IEEE Trans. Image Process..

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

[18]  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).

[19]  Marko Heikkilä,et al.  A Texture-based Method for Detecting Moving Objects , 2004, BMVC.

[20]  Venkatesh Saligrama,et al.  Foreground-Adaptive Background Subtraction , 2009, IEEE Signal Processing Letters.

[21]  Linda G. Shapiro,et al.  A new connected components algorithm for virtual memory computers , 1983, Comput. Vis. Graph. Image Process..