Fast incorporation of optical flow into active polygons

In this paper, we first reconsider, in a different light, the addition of a prediction step to active contour-based visual tracking using an optical flow and clarify the local computation of the latter along the boundaries of continuous active contours with appropriate regularizers. We subsequently detail our contribution of computing an optical flow-based prediction step directly from the parameters of an active polygon, and of exploiting it in object tracking. This is in contrast to an explicitly separate computation of the optical flow and its ad hoc application. It also provides an inherent regularization effect resulting from integrating measurements along polygon edges. As a result, we completely avoid the need of adding ad hoc regularizing terms to the optical flow computations, and the inevitably arbitrary associated weighting parameters. This direct integration of optical flow into the active polygon framework distinguishes this technique from most previous contour-based approaches, where regularization terms are theoretically, as well as practically, essential. The greater robustness and speed due to a reduced number of parameters of this technique are additional and appealing features.

[1]  Yiannis Aloimonos,et al.  Active vision , 2004, International Journal of Computer Vision.

[2]  F. Chaumette,et al.  Robust real-time visual tracking using a 2 D-3 D model-based approach , 1999 .

[3]  D. Luenberger An introduction to observers , 1971 .

[4]  Hans-Hellmut Nagel,et al.  Model-based object tracking in monocular image sequences of road traffic scenes , 1993, International Journal of Computer 11263on.

[5]  Larry S. Davis,et al.  3-D model-based tracking of humans in action: a multi-view approach , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Patrick Bouthemy,et al.  Robust real-time visual tracking using a 2D-3D model-based approach , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[7]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[8]  Rachid Deriche,et al.  Stereo matching, reconstruction and refinement of 3D curves using deformable contours , 1993, 1993 (4th) International Conference on Computer Vision.

[9]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Benjamin B. Kimia,et al.  Shapes, shocks, and deformations I: The components of two-dimensional shape and the reaction-diffusion space , 1995, International Journal of Computer Vision.

[11]  Takeo Kanade,et al.  Model-based tracking of self-occluding articulated objects , 1995, Proceedings of IEEE International Conference on Computer Vision.

[12]  Guy Rochard,et al.  Tracking and Characterization of Highly Deformable Cloud Structures , 2000, ECCV.

[13]  S. Osher,et al.  Regular Article: A PDE-Based Fast Local Level Set Method , 1999 .

[14]  Rachid Deriche,et al.  Region tracking through image sequences , 1995, Proceedings of IEEE International Conference on Computer Vision.

[15]  Anthony J. Yezzi,et al.  Information-Theoretic Active Polygons for Unsupervised Texture Segmentation , 2005, International Journal of Computer Vision.

[16]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[17]  Rachid Deriche,et al.  Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  V. Caselles,et al.  Snakes in Movement , 1996 .

[19]  Anthony J. Yezzi,et al.  A Fully Global Approach to Image Segmentation via Coupled Curve Evolution Equations , 2002, J. Vis. Commun. Image Represent..

[20]  Rama Chellappa,et al.  A generic approach to simultaneous tracking and verification in video , 2002, IEEE Trans. Image Process..

[21]  Hans-Hellmut Nagel,et al.  An Investigation of Smoothness Constraints for the Estimation of Displacement Vector Fields from Image Sequences , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  David G. Lowe,et al.  Robust model-based motion tracking through the integration of search and estimation , 1992, International Journal of Computer Vision.

[23]  Daniel Cremers,et al.  Motion Competition: A variational framework for piecewise parametric motion segmentation , 2005 .

[24]  N. Peterfreund The velocity snake: Deformable contour for tracking in spatio-velocity space , 1997 .

[25]  Sergei Fogel,et al.  The estimation of velocity vector fields from time-varying image sequences , 1991, CVGIP Image Underst..

[26]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[27]  Tony F. Chan,et al.  An Active Contour Model without Edges , 1999, Scale-Space.

[28]  Filiberto Pla,et al.  Using temporal integration for tracking regions in traffic monitoring sequences , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[29]  Natan Peterfreund,et al.  Robust Tracking of Position and Velocity With Kalman Snakes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Ellen C. Hildreth,et al.  Computations Underlying the Measurement of Visual Motion , 1984, Artif. Intell..

[31]  Marie-Odile Berger How to track efficiently piecewise curved contours with a view to reconstructing 3D objects , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[32]  S. Osher,et al.  A PDE-Based Fast Local Level Set Method 1 , 1998 .

[33]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[34]  Rama Chellappa,et al.  Estimation of Object Motion Parameters from Noisy Images , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[36]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[37]  Patrick Bouthemy,et al.  Region-Based Tracking Using Affine Motion Models in Long Image Sequences , 1994 .

[38]  Abdol-Reza Mansouri,et al.  Region Tracking via Level Set PDEs without Motion Computation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[39]  Guillermo Sapiro,et al.  Variational Problems and Partial Differential Equations on Implicit Surfaces: Bye Bye Triangulated Surfaces? , 2003 .

[40]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[41]  Guillermo Sapiro,et al.  Morphing Active Contours , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[42]  Frederic Fol Leymarie,et al.  Tracking Deformable Objects in the Plane Using an Active Contour Model , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[43]  Filiberto Pla,et al.  Motion-based segmentation and region tracking in image sequences , 2001, Pattern Recognit..

[44]  Daniel Cremers,et al.  Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation , 2005, International Journal of Computer Vision.

[45]  Edward H. Adelson,et al.  Representing moving images with layers , 1994, IEEE Trans. Image Process..

[46]  R. Deriche,et al.  Geodesic active regions for motion estimation and tracking , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[47]  Verónica Vilaplana,et al.  Face segmentation and tracking based on connected operators and partition projection , 2002, Pattern Recognit..

[48]  A. Murat Tekalp,et al.  Tracking visible boundary of objects using occlusion adaptive motion snake , 2000, IEEE Trans. Image Process..