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2018 | Supplement | Chapter

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

Authors : Ryan Cameron, Zheming Zuo, Graham Sexton, Longzhi Yang

Published in: Advances in Computational Intelligence Systems

Publisher: Springer International Publishing

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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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Title: A Fall Detection/Recognition System and an Empirical Study of Gradient-Based Feature Extraction Approaches
Authors: Ryan Cameron
Zheming Zuo
Graham Sexton
Longzhi Yang
Publisher: Springer International Publishing
Book: Advances in Computational Intelligence Systems
Print ISBN: 978-3-319-66938-0

Electronic ISBN: 978-3-319-66939-7

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-66939-7_24

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