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Part-Based RDF for Direction Classification of Pedestrians, and a Benchmark

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9009))

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Abstract

This paper proposes a new benchmark dataset for pedestrian body-direction classification, proposes a new framework for intra-class classification by directly aiming at pedestrian body-direction classification, shows that the proposed framework outperforms a state-of-the-art method,and it also proposes the use of DCT-HOG features (by combining a discrete cosine transform with the histogram of oriented gradients) as a novel approach for defining a random decision forest.

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Notes

  1. 1.

    See ccv.wordpress.fos.auckland.ac.nz/data/object-detection/.

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Authors and Affiliations

  1. The .enpeda.. Project, Tamaki Campus, The University of Auckland, Auckland, New Zealand

    Junli Tao & Reinhard Klette

Authors
  1. Junli Tao
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  2. Reinhard Klette
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Corresponding author

Correspondence to Junli Tao .

Editor information

Editors and Affiliations

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

    C.V. Jawahar

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

    Shiguang Shan

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Tao, J., Klette, R. (2015). Part-Based RDF for Direction Classification of Pedestrians, and a Benchmark. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_31

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  • DOI: https://doi.org/10.1007/978-3-319-16631-5_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16630-8

  • Online ISBN: 978-3-319-16631-5

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