Definition and Performance Evaluation of a Robust SVM Based Fall Detection Solution

2012

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 bounding box, the user's trajectory with her/his orientation, Projection Histograms and moments of order 0, 1 and 2. We study several combinations of usual transformations of the features (Fourier Transform, Wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using a si…

Fast prototyping of a SoC-based smart-camera: a real-time fall detection case study

2014

International audience; 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 detection system based on such a smart camera, which allows to reduce the development time compared to standard approaches. Founded on a supervised classification approach, we propose a HW/SW implementation to detect falls in a home environment using a single camera and an optimized descriptor adapted to real-time t…

Robust spatio-temporal descriptors for real-time SVM-based fall detection

2014

Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification

2013

We propose a supervised approach to detect falls in a home environment using an optimized descriptor adapted to real-time tasks. We introduce a realistic dataset of 222 videos, a new metric allowing evaluation of fall detection performance in a video stream, and an automatically optimized set of spatio-temporal descriptors which fed a supervised classifier. We build the initial spatio-temporal descriptor named STHF using several combinations of transformations of geometrical features (height and width of human body bounding box, the user’s trajectory with her/his orientation, projection histograms, and moments of orders 0, 1, and 2). We study the combinations of usual transformations of the…

Détection automatique de chutes de personnes basée sur des descripteurs spatio-temporels : définition de la méthode, évaluation des performances et i…

2013

We propose a supervised approach to detect falls in home environment adapted to location andpoint of view changes. First, we maid publicly available a realistic dataset, acquired in four differentlocations, containing a large number of manual annotation suitable for methods comparison. We alsodefined a new metric, adapted to real-time tasks, allowing to evaluate fall detection performance ina continuous video stream. Then, we build the initial spatio-temporal descriptor named STHF usingseveral combinations of transformations of geometrical features and an automatically optimised setof spatio-temporal descriptors thanks to an automatic feature selection step. We propose a realisticand pragma…

Spatio-temporal descriptor for SVM and Adaboost based fall detection

2013

International audience