Spatio-temporal context for codebook-based dynamic background subtraction

In background subtraction, it is challenging to detect foreground objects in the presence of dynamic background motions. The paper proposes two new algorithms to this problem by improving the codebook model with the incorporation of the spatial and temporal context of each pixel. The spatial context involves the local spatial dependency between neighboring pixels, and the temporal context involves the preceding detection result. Only the spatial context is incorporated into the first algorithm which makes the background representation more compact than the standard codebook. The second algorithm explicitly models the spatio-temporal context with a Markov random field model, thus achieving more accurate foreground detection. Extensive experiments on several dynamic scenes are conducted to compare the two proposed algorithms with each other and with the standard codebook algorithm. (C) 2009 Elsevier GmbH. All rights reserved.

[1]  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.

[2]  Shireen Elhabian,et al.  Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art , 2008 .

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

[4]  Nikos Paragios,et al.  Motion-based background subtraction using adaptive kernel density estimation , 2004, CVPR 2004.

[5]  Yi-Ping Hung,et al.  Efficient hierarchical method for background subtraction , 2007, Pattern Recognit..

[6]  Deborah Estrin,et al.  Background Subtraction on Distributions , 2008, ECCV.

[7]  Richard Szeliski,et al.  A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  W. Eric L. Grimson,et al.  Background Subtraction Using Markov Thresholds , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

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

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

[11]  Stan Z. Li,et al.  Markov Random Field Modeling in Computer Vision , 1995, Computer Science Workbench.

[12]  Yaser Sheikh,et al.  Bayesian modeling of dynamic scenes for object detection , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  L. Davis,et al.  Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002, Proc. IEEE.

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

[15]  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).

[16]  W. Eric L. Grimson,et al.  Background Subtraction for Temporally Irregular Dynamic Textures , 2008, 2008 IEEE Workshop on Applications of Computer Vision.

[17]  Stan Sclaroff,et al.  Segmenting foreground objects from a dynamic textured background via a robust Kalman filter , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[18]  Josiane Zerubia,et al.  Bayesian image classification using Markov random fields , 1996, Image Vis. Comput..