A novel algorithm to segment foreground from a similarly colored background

Abstract Color similarity between the background and the foreground causes most moving object detection algorithms to fail. This paper proposes a novel algorithm designed to segment the foreground from a similarly colored background. Central to this algorithm is that the motion cue of the moving object is useful for foreground modeling. We predict the position of the moving object in the current frame using historical motion information, and then use the prediction information to construct a predictive model. The mixture foreground model is a union of the predictive model and the general foreground model. Final segmentation is obtained by combining a likelihood modification technique and the mixture foreground model. Experimental results on typical sequences show that the proposed algorithm is efficient.

[1]  Harry Shum,et al.  Background Cut , 2006, ECCV.

[2]  A. Criminisi,et al.  Bilayer Segmentation of Live Video , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

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

[5]  Nikos Paragios,et al.  Background modeling and subtraction of dynamic scenes , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[6]  Rajeev Sharma,et al.  Adaptive texture and color segmentation for tracking moving objects , 2002, Pattern Recognit..

[7]  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.

[8]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Stefano Soatto,et al.  Dynamic Textures , 2003, International Journal of Computer Vision.

[10]  Jitendra Malik,et al.  Towards robust automatic traffic scene analysis in real-time , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[11]  Larry S. Davis,et al.  Efficient Kernel Density Estimation Using the Fast Gauss Transform with Applications to Color Modeling and Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Shyjan Mahamud,et al.  Comparing Belief Propagation and Graph Cuts for Novelty Detection , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

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

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

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

[17]  Larry S. Davis,et al.  Improved fast gauss transform and efficient kernel density estimation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[18]  Kwang-Ting Cheng,et al.  Learning a sparse, corner-based representation for time-varying background modelling , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[19]  Andrew Blake,et al.  Bi-layer segmentation of binocular stereo video , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).