Correlation-based particle filter for 3D object tracking

This manuscript deals with the problem of 3D object tracking in a multisensor framework. The object is here described by a CAD model. It avoids any image preprocessing that leads, generally, to loss of information. We develop a particle filtering method [6] that we call "correlation-based particle filter" (CBPF) to solve this non-linear estimation problem. The new proposed approach is applied to synthetic and real image sequences of complex 3D moving objects. The originality of this work consists of developing a centralized fusion method that uses, in an optimal way, the measurements delivered by the sensors. In order to optimally using the sensor outcomes, a centralized fusion approach is proposed. The method can jointly estimate 3D pose/motion parameters and track the object in the 3D domain, while many works have been developed in the image plane. Finally, we should mention that the method is not limited in terms of object structure and motion.

[1]  Luc Van Gool,et al.  An adaptive color-based particle filter , 2003, Image Vis. Comput..

[2]  Patrick Pérez,et al.  Data fusion for visual tracking with particles , 2004, Proceedings of the IEEE.

[3]  Jean-Charles Noyer,et al.  Non-linear estimation of image motion and tracking , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

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

[5]  Soon Ki Jung,et al.  A model-based 3-D tracking of rigid objects from a sequence of multiple perspective views , 1998, Pattern Recognit. Lett..

[6]  Esther Koller-Meier,et al.  Tracking multiple objects using the Condensation algorithm , 2001, Robotics Auton. Syst..

[7]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[8]  Neil J. Gordon,et al.  Editors: Sequential Monte Carlo Methods in Practice , 2001 .

[9]  Frank Ade,et al.  OBJECT DETECTION AND TRACKING IN RANGE IMAGE SEQUENCES BY SEPARATION OF IMAGE FEATURES , 1998 .

[10]  Arnaud Doucet,et al.  A survey of convergence results on particle filtering methods for practitioners , 2002, IEEE Trans. Signal Process..

[11]  Jean-Charles Noyer,et al.  Non-linear matched filtering for object detection and tracking , 2004, Pattern Recognit. Lett..

[12]  Patrick Bouthemy,et al.  A 2D-3D model-based approach to real-time visual tracking , 2001, Image Vis. Comput..

[13]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[14]  Andrea Giachetti,et al.  Matching techniques to compute image motion , 2000, Image Vis. Comput..

[15]  C. Morandi,et al.  Registration of Translated and Rotated Images Using Finite Fourier Transforms , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Hassan Foroosh,et al.  Extension of phase correlation to subpixel registration , 2002, IEEE Trans. Image Process..

[17]  Mohammed Yeasin,et al.  A 2D/3D model-based object tracking framework , 2003, Pattern Recognit..

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

[19]  André Gagalowicz,et al.  Three dimensional model-based tracking using texture learning and matching , 2000, Pattern Recognit. Lett..

[20]  Hans-Hellmut Nagel,et al.  On the Estimation of Optical Flow: Relations between Different Approaches and Some New Results , 1987, Artif. Intell..