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Efficient Detection for Spatially Local Coding

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9008))

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Abstract

In this paper, we present an efficient detector for the Spatially Local Coding (SLC) object model. SLC is a recent, high performing object classifier that has yet to be applied in a detection (object localization) setting. SLC uses features that jointly code for both appearance and location, making it difficult to apply the existing approaches to efficient detection. We design an approximate Hough transform for the SLC model that uses a cascade of thresholds followed by gradient descent to achieve efficiency as well as accurate localization. We evaluate the resulting detector on the Daimler Monocular Pedestrian dataset.

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Author information

Authors and Affiliations

  1. University of British Columbia, Vancouver, Canada

    Sancho McCann & David G. Lowe

Authors
  1. Sancho McCann
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  2. David G. Lowe
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Corresponding author

Correspondence to Sancho McCann .

Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institue of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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McCann, S., Lowe, D.G. (2015). Efficient Detection for Spatially Local Coding. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_44

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  • DOI: https://doi.org/10.1007/978-3-319-16628-5_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16627-8

  • Online ISBN: 978-3-319-16628-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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