Multi-Object Tracking Through Clutter Using Graph Cuts

The standard graph cut technique is a robust method for globally optimal image segmentations. However, because of its global nature, it is prone to capture outlying areas similar to the object of interest. This paper proposes a novel method to constrain the standard graph cut technique for tracking anywhere from one to several objects in regions of interest. For each object, we introduce a pixel penalty based upon distance from a region of interest and so segmentation is biased to remain in this area. Also, we employ a filter predicting the location of the object. The distance penalty is then centered at this location and adoptively scaled based on prediction confidence. This method is capable of tracking multiple interacting objects of different intensity profiles in both gray-scale and color imagery.

[1]  Yogesh Rathi,et al.  Shape-Based Approach to Robust Image Segmentation using Kernel PCA , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[2]  Vladimir Kolmogorov,et al.  Computing geodesics and minimal surfaces via graph cuts , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[3]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[4]  VekslerOlga,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001 .

[5]  J A Sethian,et al.  A fast marching level set method for monotonically advancing fronts. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Yogesh Rathi,et al.  Tracking Through Clutter Using Graph Cuts , 2007, BMVC.

[7]  Ning Xu,et al.  Object segmentation using graph cuts based active contours , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[8]  Daniel Cremers,et al.  Dynamical statistical shape priors for level set-based tracking , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[10]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.

[11]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[12]  Gregory G. Slabaugh,et al.  Graph cuts segmentation using an elliptical shape prior , 2005, IEEE International Conference on Image Processing 2005.

[13]  Guillermo Sapiro,et al.  O(N) implementation of the fast marching algorithm , 2006, Journal of Computational Physics.

[14]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[15]  Daniel Cremers,et al.  Shape statistics in kernel space for variational image segmentation , 2003, Pattern Recognit..

[16]  Emilio Maggio,et al.  Hybrid particle filter and mean shift tracker with adaptive transition model , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[17]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[18]  O. Faugeras,et al.  Statistical shape influence in geodesic active contours , 2002, 5th IEEE EMBS International Summer School on Biomedical Imaging, 2002..

[19]  Namrata Vaswani,et al.  Particle filtering for geometric active contours with application to tracking moving and deforming objects , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[20]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[21]  P. Kohli,et al.  Efficiently solving dynamic Markov random fields using graph cuts , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[22]  Patrick Pérez,et al.  Interactive Image Segmentation Using an Adaptive GMMRF Model , 2004, ECCV.

[23]  Yogesh Rathi,et al.  Graph Cut Segmentation with Nonlinear Shape Priors , 2007, 2007 IEEE International Conference on Image Processing.

[24]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.

[25]  Stefano Soatto,et al.  A lagrangian formulation of nonholonomic path following , 1997, Block Island Workshop on Vision and Control.

[26]  Tao Zhang,et al.  Interactive graph cut based segmentation with shape priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[27]  Yogesh Rathi,et al.  A Graph Cut Approach to Image Segmentation in Tensor Space , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.