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International Conference on Computer Analysis of Images and Patterns

CAIP 2017: Computer Analysis of Images and Patterns pp 43–54Cite as

Mixing Hough and Color Histogram Models for Accurate Real-Time Object Tracking

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10424))

Abstract

This paper presents a new object tracking algorithm, which does not rely on offline supervised learning. We propose a very fast and accurate tracker, exclusively based on two complementary low-level features: gradient-based and color-based features. On the first hand, we compute a Generalized Hough Transform, indexed by gradient orientation. On the second hand, a RGB color histogram is used as a global rotation-invariant model. These two parts are processed independently, then merged to estimate the object position. Then, two confidence maps are generated and combined to estimate the object size. Experiments made on VOT2014 and VOT2015 datasets show that our tracker is competitive among all competitors (in accuracy and robustness, ranked in the top 10 and top 15 respectively), and is one of the few trackers running at more than 100 fps on a laptop machine, with one thread. Thanks to its low memory footprint, it can also run on embedded systems.

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Acknowledgments

We gratefully acknowledge financial support from the French Government Defense procurement and technology agency (DGA/MRIS).

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Authors and Affiliations

  1. U2IS, ENSTA ParisTech, Université de Paris-Saclay, 828, Bd des Maréchaux, 91762, Palaiseau Cedex, France

    Antoine Tran & Antoine Manzanera

Authors
  1. Antoine Tran
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  2. Antoine Manzanera
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Correspondence to Antoine Tran .

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Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Michael Felsberg

  2. Lund University, Lund, Sweden

    Anders Heyden

  3. University of Southern Denmark, Odense, Denmark

    Norbert Krüger

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Tran, A., Manzanera, A. (2017). Mixing Hough and Color Histogram Models for Accurate Real-Time Object Tracking. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10424. Springer, Cham. https://doi.org/10.1007/978-3-319-64689-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-64689-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64688-6

  • Online ISBN: 978-3-319-64689-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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