Algorithmic Fusion for More Robust Feature Tracking

We present a framework for merging the results of independent feature-based motion trackers using a classification based approach. We demonstrate the efficacy of the framework using corner trackers as an example. The major problem with such systems is generating ground truth data for training. We show how synthetic data can be used effectively to overcome this problem. Our combined system performs better in both dropouts and errors than a correspondence tracker, and had less than half the dropouts at the cost of moderate increase in error compared to a relaxation tracker.

[1]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[2]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[3]  Robin R. Murphy,et al.  Biological and cognitive foundations of intelligent sensor fusion , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[4]  Richard Szeliski,et al.  A parallel feature tracker for extended image sequences , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

[5]  Hans-Hellmut Nagel,et al.  Estimation of Optical Flow Based on Higher-Order Spatiotemporal Derivatives in Interlaced and Non-Interlaced Image Sequences , 1995, Artif. Intell..

[6]  Sargur N. Srihari,et al.  Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Brendan McCane,et al.  On Benchmarking Optical Flow , 2001, Comput. Vis. Image Underst..

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

[9]  B. McCane,et al.  OSCAR: object segmentation using correspondence and relaxation , 1999, Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062).

[10]  Mario Vento,et al.  Multiclassification: reject criteria for the Bayesian combiner , 1999, Pattern Recognit..

[11]  Amir Averbuch,et al.  Interacting Multiple Model Methods in Target Tracking: A Survey , 1988 .

[12]  Ingemar J. Cox,et al.  An Efficient Implementation of Reid's Multiple Hypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Ingemar J. Cox,et al.  A review of statistical data association techniques for motion correspondence , 1993, International Journal of Computer Vision.

[14]  Stephen M. Smith,et al.  ASSET-2: real-time motion segmentation and shape tracking , 1995, Proceedings of IEEE International Conference on Computer Vision.

[15]  William H. Press,et al.  Numerical recipes in C , 2002 .

[16]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.