A Probabilistic Exclusion Principle for Tracking Multiple Objects

Tracking multiple targets is a challenging problem, especially when the targets are “identical”, in the sense that the same model is used to describe each target. In this case, simply instantiating several independent 1-body trackers is not an adequate solution, because the independent trackers tend to coalesce onto the best-fitting target. This paper presents an observation density for tracking which solves this problem by exhibiting a probabilistic exclusion principle. Exclusion arises naturally from a systematic derivation of the observation density, without relying on heuristics. Another important contribution of the paper is the presentation of partitioned sampling, a new sampling method for multiple object tracking. Partitioned sampling avoids the high computational load associated with fully coupled trackers, while retaining the desirable properties of coupling.

[1]  D. Mumford,et al.  Stochastic models for generic images , 2001 .

[2]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[3]  Andrew Blake,et al.  A probabilistic contour discriminant for object localisation , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[4]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[5]  Timothy F. Cootes,et al.  Active shape models , 1998 .

[6]  Ali Esmaili,et al.  Probability and Random Processes , 2005, Technometrics.

[7]  Rachid Deriche,et al.  A PDE-based level-set approach for detection and tracking of moving objects , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[8]  Natan Peterfreund,et al.  Robust tracking with spatio-velocity snakes: Kalman filtering approach , 1997, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[9]  Kanti V. Mardia,et al.  CONDITIONAL CYCLIC MARKOV RANDOM FIELDS , 1996 .

[10]  P. Moral.  Measure-valued processes and interacting particle systems. Application to nonlinear filtering problems , 1998 .

[11]  P. Anandan,et al.  A Unified Approach to Moving Object Detection in 2D and 3D Scenes , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

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

[13]  Michael Isard,et al.  Active Contours , 2000, Springer London.

[14]  Gerhard Winkler,et al.  Image analysis, random fields and dynamic Monte Carlo methods: a mathematical introduction , 1995, Applications of mathematics.

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

[16]  Andrew Blake,et al.  Spatial Dependence in the Observation of Visual Contours , 1998, ECCV.

[17]  MumfordDavid,et al.  Filters, Random Fields and Maximum Entropy (FRAME) , 1998 .

[18]  P. Anandan,et al.  A unified approach to moving object detection in 2D and 3D scenes , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[19]  L. Davis,et al.  W 4 S: a Real-time System for Detecting and Tracking People in 2 1 2 D , 1998 .

[20]  Geoffrey E. Hinton,et al.  Adaptive Elastic Models for Hand-Printed Character Recognition , 1991, NIPS.

[21]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[22]  David Mumford,et al.  The 2.1-D sketch , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[23]  Song-Chun Zhu,et al.  Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling , 1998, International Journal of Computer Vision.

[24]  P. Fearnhead,et al.  An improved particle filter for non-linear problems , 1999 .

[25]  N. Gordon.  A hybrid bootstrap filter for target tracking in clutter , 1995, IEEE Transactions on Aerospace and Electronic Systems.

[26]  Richard Szeliski,et al.  Physically based and probabilistic models for computer vision , 1991, Optics & Photonics.

[27]  Simon J. Godsill,et al.  On sequential simulation-based methods for Bayesian filtering , 1998 .

[28]  Anil K. Jain,et al.  Markov random fields : theory and application , 1993 .

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

[30]  Jitendra Malik,et al.  Robust Multiple Car Tracking with Occlusion Reasoning , 1994, ECCV.

[31]  Heinrich Niemann,et al.  Adaptive Road Recognition and Ego-state Tracking in the Presence of Obstacles , 1998, International Journal of Computer Vision.

[32]  Larry S. Davis,et al.  W4S : A real-time system for detecting and tracking people in 2 D , 1998, eccv 1998.

[33]  Patrick Billingsley,et al.  Probability and Measure. , 1986 .

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

[35]  Larry S. Davis,et al.  W4S: A real-time system detecting and tracking people in 2 1/2D , 1998, ECCV.

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

[37]  James W. Davis,et al.  Real-time closed-world tracking , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[38]  David C. Hogg,et al.  Learning Flexible Models from Image Sequences , 1994, ECCV.

[39]  Jun S. Liu,et al.  Sequential Imputations and Bayesian Missing Data Problems , 1994 .

[40]  M. Irani,et al.  A Uni ed Approach to Moving Object Detection in D and D Scenes , 1998 .

[41]  Jun S. Liu,et al.  Blind Deconvolution via Sequential Imputations , 1995 .

[42]  Gregory D. Hager,et al.  Joint probabilistic techniques for tracking multi-part objects , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).