Unscented Kalman filter for visual curve tracking

Abstract Visual contour tracking in complex background is a difficult task. The measurement model is often nonlinear due to clutter in images. Traditional visual trackers based on Kalman filters employ simple linear measurement models, and often collapse during the tracking process. This paper presents a new contour tracker based on unscented Kalman filter that is superior to extended Kalman filter both in theory and in many practical situations. The new tracker employs a more accurate nonlinear measurement model, without computation of a Jacobian matrix. During each time step, the tracker makes multiple measurements in terms of the set of appropriately chosen sample points, thus obtaining the best observation according to the measurement density. The resulting algorithm is able to obtain a more exact estimate of the state of the system, while having the same order of complexity as that of an extend Kalman Filter. The experiments show that the new algorithm is superior to those based on Kalman filters.

[1]  Andrew Blake,et al.  A framework for spatiotemporal control in the tracking of visual contours , 1993, International Journal of Computer Vision.

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

[3]  Michael Isard,et al.  Object localization by Bayesian correlation , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[4]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[5]  Michael Isard,et al.  Learning to Track the Visual Motion of Contours , 1995, Artif. Intell..

[6]  Simon J. Julier,et al.  The scaled unscented transformation , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[7]  Peihua Li,et al.  Visual contour tracking based on sequential importance sampling/resampling algorithm , 2002, Object recognition supported by user interaction for service robots.

[8]  Richard Szeliski,et al.  Tracking with Kalman snakes , 1993 .

[9]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.

[10]  Michael Isard,et al.  Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking , 2000, ECCV.

[11]  Alex Pentland,et al.  Computer Vision for Human–Machine Interaction: Acknowledgements , 1998 .

[12]  R. Cipolla,et al.  Computer Vision for Human–Machine Interaction: Tracking Faces , 1998 .

[13]  Natan Peterfreund.  The Velocity Snake: Deformable Contour for Tracking in Spatio-Velocity Space , 1999, Comput. Vis. Image Underst..

[14]  Yongmin Kim,et al.  Active contour model with gradient directional information: directional snake , 2001, IEEE Trans. Circuits Syst. Video Technol..

[15]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

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

[17]  Roberto Cipolla,et al.  Human-robot interface by pointing with uncalibrated stereo vision , 1996, Image Vis. Comput..

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

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

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