Special Section on Video Surveillance and Transportation Imaging Applications

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

[+] Author Affiliations
Imen Charfi

Université de Bourgogne, Laboratoire d’électronique, d’informatique et de l’image (Le2i), Faculté Mirande, 21000 Dijon, France

Laboratoire d’électronique et de microelectronique (LEME), Faculté des sciences de Monastir, Tunisie

Johel Miteran

Université de Bourgogne, Laboratoire d’électronique, d’informatique et de l’image (Le2i), Faculté Mirande, 21000 Dijon, France

Julien Dubois

Université de Bourgogne, Laboratoire d’électronique, d’informatique et de l’image (Le2i), Faculté Mirande, 21000 Dijon, France

Mohamed Atri

Laboratoire d’électronique et de microelectronique (LEME), Faculté des sciences de Monastir, Tunisie

Rached Tourki

Laboratoire d’électronique et de microelectronique (LEME), Faculté des sciences de Monastir, Tunisie

J. Electron. Imaging. 22(4), 041106 (Jul 22, 2013). doi:10.1117/1.JEI.22.4.041106
History: Received March 8, 2013; Revised June 10, 2013; Accepted June 14, 2013
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Abstract.  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 features (Fourier transform, wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using support vector machine and Adaboost classifiers. Automatic feature selection allows to show that the best tradeoff between classification performance and processing time is obtained by combining the original low-level features with their first derivative. Hence, we evaluate the robustness of the fall detection regarding location changes. We propose a realistic and pragmatic protocol that enables performance to be improved by updating the training in the current location with normal activities records.

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© 2013 SPIE and IS&T

Citation

Imen Charfi ; Johel Miteran ; Julien Dubois ; Mohamed Atri and Rached Tourki
"Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification", J. Electron. Imaging. 22(4), 041106 (Jul 22, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.4.041106


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