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.
Highlights
The increasing number of aged persons has led to the uncertainty of unaided and unprovoked falls which may cause physical harm, injuries and health deterioration
We propose an algorithm under a boosting framework based on RGB video data for human fall detection
The UR Fall Detection (URFD) dataset contains frontal and overhead video sequences obtained by two Kinect sensors, with one placed at the height of 1 m from the floor and the other mounted on the ceiling with a height of 3 m
Summary
The increasing number of aged persons has led to the uncertainty of unaided and unprovoked falls which may cause physical harm, injuries and health deterioration. These problems may become more intense if timely aid and assistance is not available. To mitigate such effects and to control the risks, there must be an accurate fall detection system. The development of an intelligent surveillance system is essential, a system which has the capacity to automatically detect fall incidences using surveillance cameras. In Proceedings of the British Machine Vision Conference (BMVC 2017), London, UK, 4–7 September 2017
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