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Vehicle Detection Using Approximation of Feature Pyramids in the DFT Domain

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Image Analysis and Recognition (ICIAR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9164))

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Abstract

Multi-resolution vehicle detection usually requires extracting a certain kind of features from each scale of an image pyramid to construct a feature pyramid, which is considered as a computational bottleneck for many object detectors. In this paper, a novel technique for the approximation of feature pyramids by using feature resampling in the 2D discrete Fourier transform domain is presented. Experimental results show that the proposed scheme provides higher detection accuracy than that provided by the state-of-the-art techniques on two sequences from LISA 2010 dataset, while maintaining the real-time detection speed.

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Notes

  1. 1.

    http://cbcl.mit.edu/software-datasets/streetscenes.

  2. 2.

    Code: http://vision.ucsd.edu/~pdollar/toolbox/.

  3. 3.

    Supplementary material shows several detection qualitative results https://www.youtube.com/watch?v=y5-m9c4TJMY.

  4. 4.

    The test is carried out on a PC with 2.9 GHz CPU.

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Acknowledgments

This work was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada and in part by the Regroupement Stratégique en Microélectronique du Québec (ReSMiQ).

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada

    Mohamed A. Naiel, M. Omair Ahmad & M. N. S. Swamy

Authors
  1. Mohamed A. Naiel
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  2. M. Omair Ahmad
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  3. M. N. S. Swamy
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Corresponding author

Correspondence to M. Omair Ahmad .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, Ontario, Canada

    Mohamed Kamel

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

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Naiel, M.A., Omair Ahmad, M., Swamy, M.N.S. (2015). Vehicle Detection Using Approximation of Feature Pyramids in the DFT Domain. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_47

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  • DOI: https://doi.org/10.1007/978-3-319-20801-5_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20800-8

  • Online ISBN: 978-3-319-20801-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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