Computer vision based fall detection by a convolutional neural network

Yu, Miao (2017) Computer vision based fall detection by a convolutional neural network. In: 19th ACM International Conference on Multimodal Interaction, 13 - 17 November 2017, Glasgow, Scotland.

ICMI fall detection.pdf
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Item Type:Conference or Workshop contribution (Poster)
Item Status:Live Archive


In this work, we propose a novel computer vision based fall detection
system, which could be applied for the health-care of the
elderly people community. For a recorded video stream, background
subtraction is firstly applied to extract the human body silhouette.
Extracted silhouettes corresponding to daily activities are applied
to construct a convolutional neural network, which is applied for
classification of different classes of human postures (e.g., bend,
stand, lie and sit) and detection of a fall event (i.e., lying posture
is detected in the floor region). As far as we know, this work is
the first attempt for the application of the convolutional neural
network for the fall detection application. From a dataset of daily
activities recorded from multiple people, we show that the proposed
method both achieves higher postures classification results than
the state-of-the-art classifiers and can successfully detect the fall
event with a low false alarm rate.

Keywords:Healthcare, fall detection, convolutional neural network, classification
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
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ID Code:29437
Deposited On:08 Nov 2017 15:14

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