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

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 accu...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 18; no. 6; p. 1918
Main Authors:	Ali, Syed Farooq, Khan, Reamsha, Mahmood, Arif, Hassan, Malik Tahir, Jeon, And Moongu
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 12-06-2018 MDPI
Subjects:	Cameras Covariance Damage detection Datasets Fall detection health and well-being human fall detection Human motion intelligent surveillance systems Real time safety and security intelligent surveillance systems human fall detection safety and security health and well-being
Online Access:	Get full text
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Summary:	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 ( ) and 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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1424-8220 1424-8220
DOI:	10.3390/s18061918