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

Ryan Cameron, Zheming Zuo, Graham Sexton, Longzhi Yang

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems
EditorsFei Chao, Steven Schockaert, Qingfu Zhang
Place of PublicationCham
PublisherSpringer
Pages276-289
Volume650
ISBN (Electronic)9783319669397
ISBN (Print)9783319669380
DOIs
Publication statusPublished - 1 Jan 2018

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer
ISSN (Electronic)2194-5357

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