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Sensors 2018, 18(6), 1918; https://doi.org/10.3390/s18061918

Using Temporal Covariance of Motion and Geometric Features via Boosting for Human Fall Detection

1
Department of Software Engineering, University of Management and Technology, UMT Road, C-II Johar Town, Lahore 54000, Pakistan
2
Department of Computer Science, Information Technology University (ITU), 346-B, Ferozepur Road, Lahore, Punjab 54000, Pakistan
3
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea
*
Author to whom correspondence should be addressed.
Received: 9 April 2018 / Revised: 28 May 2018 / Accepted: 11 June 2018 / Published: 12 June 2018
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Abstract

Fall induced damages are serious incidences for aged as well as young persons. A real-time automatic and accurate fall detection system can play a vital role in timely medication care which will ultimately help to decrease the damages and complications. In this paper, we propose a fast and more accurate real-time system which can detect people falling in videos captured by surveillance cameras. Novel temporal and spatial variance-based features are proposed which comprise the discriminatory motion, geometric orientation and location of the person. These features are used along with ensemble learning strategy of boosting with J48 and Adaboost classifiers. Experiments have been conducted on publicly available standard datasets including Multiple Cameras Fall (with 2 classes and 3 classes) and UR Fall Detection achieving percentage accuracies of 99.2, 99.25 and 99.0, respectively. Comparisons with nine state-of-the-art methods demonstrate the effectiveness of the proposed approach on both datasets. View Full-Text
Keywords: intelligent surveillance systems; human fall detection; health and well-being; safety and security intelligent surveillance systems; human fall detection; health and well-being; safety and security
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Ali, S.F.; Khan, R.; Mahmood, A.; Hassan, M.T.; Jeon, M. Using Temporal Covariance of Motion and Geometric Features via Boosting for Human Fall Detection. Sensors 2018, 18, 1918.

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