Multi-view fall detection based on spatio-temporal interest points

Songzhi Su

doi:10.1007/s11042-015-2766-3

Multi-view fall detection based on spatio-temporal interest points

2015-07-11

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DOI：10.1007/s11042-015-2766-3

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Many countries are experiencing a rapid increase in their elderly populations, increasing the demand for appropriate healthcare systems including fall-detection systems. In recent years, many fall-detection systems have been developed, although most require the use of wearable devices. Such systems function only when the subject is wearing the device. A vision-based system presents a more convenient option. However, visual features typically depend on camera view; a single, fixed camera may not properly identify falls occurring in various directions. Thus, this study presents a solution that involves using multiple cameras. The study offers two main contributions. First, in contrast to most vision-based systems that analyze silhouettes to detect falls, the present system proposes a novel feature for measuring the degree of impact shock that is easily detectable with a wearable device but more difficult with a computer vision system. In addition, the degree of impact shock is less sensitive to camera views and can be extracted more robustly than a silhouette. Second, the proposed method uses a majority-voting strategy based on multiple views to avoid performing the tedious camera calibration required by most multiple-camera approaches. Specifically, the proposed method is based on spatio-temporal interest points (STIPs). The number of local STIP clusters is designed to indicate the degree of impact shock and body vibration. Sequences of these features are concatenated into feature vectors that are then fed into a support vector machine to classify the fall event. A majority-voting strategy based on multiple views is then used for the final determination. The proposed method has been applied to a publicly available dataset to offer evidence that the proposed method outperforms existing methods based on the same data input.

Keywords:

Fall detection

Impact shock

Spatio-temporal interest points

Human silhouette

Multiple-view

Foreground segmentation

Camera calibration

Multi-view fall detection based on spatio-temporal interest points

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