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We propose an automatic approach to detect falls in home environment. A Support Vector Machine based classifier is fed by a set of selected features extracted from human body silhouette tracking. The classifier is followed by filtering operations taking into account the temporal nature of a video. The features are based on height and width of human body(More)
Smart camera, i.e. cameras that are able to acquire and process images in real-time, is a typical example of the new embedded computer vision systems. A key example of application is automatic fall detection, which can be useful for helping elderly people in daily life. In this paper, we propose a methodology for development and fast-prototyping of a fall(More)
We propose a SVM-based approach to detect falls in several home environments using an optimised descriptor adapted to real-time tasks.We build an optimised spatio-temporal descriptor named STHF<sub>a_SBFS</sub> using several combinations of transformations of geometrical features, thanks to feature selection. We study the combinations of usual(More)
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