Robust contour-based object tracking integrating color and edge likelihoods

We present in this paper a novel object tracking system based on 3D contour models. For this purpose, we integrate two complimentary likelihoods, defined on local color statistics and intensity edges, into a common nonlinear estimation problem. The proposed method improves robustness and adaptivity with respect to challenging background and light conditions, and can be extended to multiple calibrated cameras. In order to achieve real-time capabilities for complex models, we also integrate in this framework a GPU-accelerated contour sampler, which quickly selects feature points and deals with generic shapes including polyhedral, non-convex as well as smooth surfaces, represented by polygonal meshes.

[1]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[2]  Alois Knoll,et al.  An Efficient and Robust Real-Time Contour Tracking System , 2006, Fourth IEEE International Conference on Computer Vision Systems (ICVS'06).

[3]  Roberto Cipolla,et al.  Real-Time Visual Tracking of Complex Structures , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  F. Frances Yao,et al.  Binary partitions with applications to hidden surface removal and solid modelling , 1989, SCG '89.

[5]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[6]  Chris Harris,et al.  Tracking with rigid models , 1993 .

[7]  Michael Beetz,et al.  The Contracting Curve Density Algorithm: Fitting Parametric Curve Models to Images Using Local Self-Adapting Separation Criteria , 2004, International Journal of Computer Vision.

[8]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[9]  Andrew Zisserman,et al.  Robust Object Tracking , 2001 .

[10]  Richard M. Murray,et al.  A Mathematical Introduction to Robotic Manipulation , 1994 .

[11]  Roberto Cipolla,et al.  Visual tracking and control using Lie algebras , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[12]  John F. Hughes,et al.  Hardware-determined feature edges , 2004, NPAR '04.

[13]  Stanley T. Birchfield,et al.  Elliptical head tracking using intensity gradients and color histograms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[14]  Alois Knoll,et al.  Sampling feature points for contour tracking with graphics hardware , 2008, VMV.

[15]  Roger Fletcher,et al.  Practical methods of optimization; (2nd ed.) , 1987 .

[16]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.