A Bag of Features Approach to Ambient Fall Detection for Domestic Elder-care

Abstract

Falls in the home are a major source of injury for the elderly. The affordability of commodity video cameras is prompting the development of ambient intelligent environments to monitor the occurence of falls in the home. This paper describes an automated fall detection system, capable of tracking movement and detecting falls in real-time. In particular we explore the application of the Bag of Features paradigm, frequently applied to general activity recognition in Computer Vision, to the domestic fall detection problem. We show that fall detection is feasible using such a framework, evaluted our approach in both controlled test scenarios and domestic scenarios exhibiting uncontrolled fall direction and visually cluttered environments.

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