We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Using Temporal Covariance of Motion and Geometric Features via Boosting for Human Fall Detection.
- Authors
Ali, Syed Farooq; Khan, Reamsha; Mahmood, Arif; Hassan, Malik Tahir; Jeon, Moongu
- 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 <italic>Multiple Cameras Fall</italic> (<italic>with 2 classes and 3 classes</italic>) and <italic>UR Fall Detection</italic> 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.
- Subjects
ACCIDENTAL falls; ANALYSIS of covariance; TELEVISION in security systems; MACHINE learning; VIDEO recording
- Publication
Sensors (14248220), 2018, Vol 18, Issue 6, p1918
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s18061918