Motion tracking as spatio-temporal motion boundary detection

Abstract The purpose of this study is to investigate tracking of moving objects in a sequence of images by detecting the surface generated by motion boundaries in the space–time domain. Estimation of this spatio-temporal surface is formulated as a Bayesian image partitioning problem. Minimization of the resulting energy functional seeks a solution biased toward smooth closed surfaces which coincide with motion boundaries, have small area, and partition the image into regions of contrasting motion activity. The Euler–Lagrange partial differential equations of minimization are expressed as level set evolution equations to obtain a topology independent and numerically stable algorithm. The formulation does not require estimation of the image motion field or assume a known background. It allows multiple non-simultaneous independent motions to occur and can account for camera motion without prior estimation of this motion. The analysis assumes short-range image motion. With moving cameras, it assumes that this short-range motion varies smoothly everywhere except across motion boundaries.

[1]  Stanley Osher,et al.  Level Set Methods , 2003 .

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

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

[4]  Naonori Ueda,et al.  Tracking Moving Contours Using Energy-Minimizing Elastic Contour Models , 1992, ECCV.

[5]  T. Michael Knasel,et al.  Robotics and autonomous systems , 1988, Robotics Auton. Syst..

[6]  Demetri Terzopoulos,et al.  Physically based models with rigid and deformable components , 1988, IEEE Computer Graphics and Applications.

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

[8]  Michael J. Black.  Combining Intensity and Motion for Incremental Segmentation and Tracking Over Long Image Sequences , 1992, ECCV.

[9]  Rama Chellappa,et al.  Automatic feature point extraction and tracking in image sequences for arbitrary camera motion , 1995, International Journal of Computer Vision.

[10]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..

[11]  Alex Pentland,et al.  Automatic extraction of deformable part models , 1990, International Journal of Computer Vision.

[12]  Manfredo P. do Carmo,et al.  Differential geometry of curves and surfaces , 1976 .

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

[14]  Janusz Konrad,et al.  Minimum description length region tracking with level sets , 2000, Electronic Imaging.

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

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

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

[18]  J.K. Aggarwal,et al.  Correspondence processes in dynamic scene analysis , 1981, Proceedings of the IEEE.

[19]  D. Chopp.  Computing Minimal Surfaces via Level Set Curvature Flow , 1993 .

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

[21]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

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

[23]  J. Sethian,et al.  A Fast Level Set Method for Propagating Interfaces , 1995 .

[24]  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.

[25]  Guillermo Sapiro,et al.  Morphing active contours: a geometric approach to topology-independent image segmentation and tracking , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[26]  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..

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

[28]  Roman Goldenberg,et al.  Fast Geodesic Active Contours , 1999, Scale-Space.