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

Cameron, Ryan, Zuo, Zheming, Sexton, Graham and Yang, Longzhi (2017) A Fall Detection/Recognition System and an Empirical Study of Gradient-Based Feature Extraction Approaches. In: Advances in Computational Intelligence Systems. Advances in Intelligent Systems and Computing, 650 . Springer, pp. 276-289. ISBN 978-3-319-66938-0

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Official URL: https://doi.org/10.1007/978-3-319-66939-7_24

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.

Item Type: Book Section
Uncontrolled Keywords: Fall detection, Local feature extraction, HOG, HMG, HOF, MBH, Artificial neural network
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 05 Oct 2017 15:11
Last Modified: 05 Oct 2017 15:11
URI: http://nrl.northumbria.ac.uk/id/eprint/32198

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