Probabilistic detection and tracking of motion discontinuities

We propose a Bayesian framework for representing and recognizing local image motion in terms of two primitive models: translation and motion discontinuity. Motion discontinuities are represented using a nonlinear generative model that explicitly encodes the orientation of the boundary, the velocities on either side, the motion of the occluding edge over time, and the appearance/disappearance of pixels at the boundary. We represent the posterior distribution over the model parameters given the image data using discrete samples. This distribution is propagated over time using the Condensation algorithm. To efficiently represent such a high-dimensional space we initialize samples using the responses of a low-level motion discontinuity detector.

[1]  J. Potter.  Scene segmentation using motion information , 1977 .

[2]  Valdis Berzins,et al.  Dynamic Occlusion Analysis in Optical Flow Fields , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  William B. Thompson,et al.  Analysis of Accretion and Deletion at Boundaries in Dynamic Scenes , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Anselm Spoerri,et al.  The early detection of motion boundaries , 1990, ICCV 1987.

[5]  Brian G. Schunck,et al.  Image Flow Segmentation and Estimation by Constraint Line Clustering , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Michael J. Black,et al.  Constraints for the Early Detection of Discontinuity from Motion , 1990, AAAI.

[7]  Michael J. Black,et al.  Mixture models for optical flow computation , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Keith Langley,et al.  Computational analysis of non-Fourier motion , 1994, Vision Research.

[9]  Sourabh A. Niyogi,et al.  Detecting kinetic occlusion , 1995, Proceedings of IEEE International Conference on Computer Vision.

[10]  George T. Chou,et al.  A model of figure-ground segregation from kinetic occlusion , 1995, Proceedings of IEEE International Conference on Computer Vision.

[11]  Harpreet S. Sawhney,et al.  Layered representation of motion video using robust maximum-likelihood estimation of mixture models and MDL encoding , 1995, Proceedings of IEEE International Conference on Computer Vision.

[12]  Harpreet S. Sawhney,et al.  Compact Representations of Videos Through Dominant and Multiple Motion Estimation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

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

[14]  Edward H. Adelson,et al.  A unified mixture framework for motion segmentation: incorporating spatial coherence and estimating the number of models , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Michael J. Black,et al.  The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields , 1996, Comput. Vis. Image Underst..

[16]  David Suter,et al.  Optic flow calculation using robust statistics , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  David J. Fleet,et al.  Motion feature detection using steerable flow fields , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[18]  Michael Isard,et al.  A mixed-state condensation tracker with automatic model-switching , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[19]  Michael Isard,et al.  ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework , 1998, ECCV.

[20]  Michael J. Black,et al.  A Probabilistic Framework for Matching Temporal Trajectories: CONDENSATION-Based Recognition of Gestures and Expressions , 1998, ECCV.

[21]  Alan L. Yuille,et al.  Visual motion estimation and prediction: a probabilistic network model for temporal coherence , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[22]  William T. Freeman,et al.  Learning to Estimate Scenes from Images , 1998, NIPS.

[23]  Michael J. Black.  Explaining optical flow events with parameterized spatio-temporal models , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).