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A Fall Detection/Recognition System and an Empirical Study of Gradient-Based Feature Extraction Approaches

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Advances in Computational Intelligence Systems (UKCI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 650))

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Abstract

Physically falling down amongst the elder helpless party is one of the most intractable issues in the era of ageing society, which has attracted intensive attentions in academia ranging from clinical research to computer vision studies. This paper proposes a fall detection/recognition system within the realm of computer vision. The proposed system integrates a group of gradient-based local visual feature extraction approaches, including histogram of oriented gradients (HOG), histogram of motion gradients (HMG), histogram of optical flow (HOF), and motion boundary histograms (MBH). A comparative study of the descriptors with the support of an artificial neural network was conducted based on an in-house captured dataset. The experimental results demonstrated the effectiveness of the proposed system and the power of these descriptors in real-world applications.

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Authors and Affiliations

  1. Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK

    Ryan Cameron, Zheming Zuo, Graham Sexton & Longzhi Yang

Authors
  1. Ryan Cameron
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  2. Zheming Zuo
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  3. Graham Sexton
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  4. Longzhi Yang
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Corresponding author

Correspondence to Longzhi Yang .

Editor information

Editors and Affiliations

  1. Xiamen University, Xiamen Shi, Fujian, China

    Fei Chao

  2. Cardiff University, Cardiff, United Kingdom

    Steven Schockaert

  3. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

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Cameron, R., Zuo, Z., Sexton, G., Yang, L. (2018). A Fall Detection/Recognition System and an Empirical Study of Gradient-Based Feature Extraction Approaches. In: Chao, F., Schockaert, S., Zhang, Q. (eds) Advances in Computational Intelligence Systems. UKCI 2017. Advances in Intelligent Systems and Computing, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-66939-7_24

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  • DOI: https://doi.org/10.1007/978-3-319-66939-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66938-0

  • Online ISBN: 978-3-319-66939-7

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