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Hough-Based Tracking of Deformable Objects

  • Chapter
Decision Forests for Computer Vision and Medical Image Analysis

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Online learning has shown to be successful in tracking-by-detection of previously unknown objects. However, most approaches are limited to a bounding box representation with fixed aspect ratio and cannot handle highly non-rigid and articulated objects. Moreover, they provide only a limited foreground/background separation, which in turn, increases the amount of noise introduced during online self-training. To overcome this limitation, we present a tracking-by-detection approach based on the generalized Hough transform. We extend the idea of Hough forests (Chap. 11) to the online domain, and couple the voting-based detection and back-projection with a rough GrabCut segmentation (Rother et al. in ACM Trans. Graph. 23(3), 2004). This significantly reduces the amount of noisy training samples during online learning and thus effectively prevents the tracker from drifting. To show these benefits, we demonstrate our method for tracking a variety of previously unknown objects, even under heavy non-rigid transformations, partial occlusions, scale changes and rotations. Moreover, we compare our tracker to state-of-the-art methods including both bounding box-based and part-based trackers.

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Notes

  1. 1.

    Implementation from http://opencv.willowgarage.com.

  2. 2.

    http://lrs.icg.tugraz.at/research/houghtrack/.

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Acknowledgements

The work was supported by the Austrian Research Promotion Agency (FFG) projects MobiTrick (8258408) in the FIT-IT program and SHARE (831717) in the IV2Splus program, and by the Austrian Science Foundation (FWF) project Advanced Learning for Tracking and Detection in Medical Workflow Analysis (I535-N23).

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Authors and Affiliations

  1. Graz University of Technology, Graz, Austria

    M. Godec, P. M. Roth & H. Bischof

Authors
  1. M. Godec
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  2. P. M. Roth
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  3. H. Bischof
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Editor information

Editors and Affiliations

  1. Microsoft Research Ltd., 7 J.J. Thomson Avenue, Cambridge, CB3 0FB, United Kingdom

    A. Criminisi

  2. Microsoft Research Ltd., 7 J.J. Thomson Avenue, Cambridge, CB3 0FB, United Kingdom

    J. Shotton

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Godec, M., Roth, P.M., Bischof, H. (2013). Hough-Based Tracking of Deformable Objects. In: Criminisi, A., Shotton, J. (eds) Decision Forests for Computer Vision and Medical Image Analysis. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4929-3_12

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  • DOI: https://doi.org/10.1007/978-1-4471-4929-3_12

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4928-6

  • Online ISBN: 978-1-4471-4929-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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