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Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 Workshops pp 405–419Cite as

Cost-Based Feature Transfer for Vehicle Occupant Classification

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10116))

Abstract

Knowledge of human presence and interaction in a vehicle is of growing interest to vehicle manufacturers for design and safety purposes. We present a framework to perform the tasks of occupant detection and occupant classification for automatic child locks and airbag suppression. It operates for all passenger seats using a single overhead camera. A transfer learning technique is introduced to make full use of training data from all seats, whilst still maintaining some control over the bias necessary for a system designed to penalize certain misclassifications more than others. An evaluation is performed on a challenging dataset with both weighted and unweighted classifiers that demonstrates the effectiveness of the transfer process.

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Notes

  1. 1.

    The steer for this work comes from Jaguar Land Rover Research.

  2. 2.

    We developed our own implementation of [23] as faithfully as possible.

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Author information

Authors and Affiliations

  1. Visual Information Laboratory, University of Bristol, Bristol, UK

    Toby Perrett & Majid Mirmehdi

Authors
  1. Toby Perrett
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  2. Majid Mirmehdi
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Corresponding author

Correspondence to Toby Perrett .

Editor information

Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Perrett, T., Mirmehdi, M. (2017). Cost-Based Feature Transfer for Vehicle Occupant Classification. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10116. Springer, Cham. https://doi.org/10.1007/978-3-319-54407-6_27

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  • DOI: https://doi.org/10.1007/978-3-319-54407-6_27

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  • Print ISBN: 978-3-319-54406-9

  • Online ISBN: 978-3-319-54407-6

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