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Optimised spatio-temporal descriptors for real-time fall detection : comparison of SVM and Adaboost based classification

Abstract : We propose a supervised approach to detect falls in home environment using an optimized descriptor adapted to real-time tasks. We introduce a realistic dataset of 222 videos, a new metric allowing to evaluate 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 vectormachine 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 activitiesrecords.
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https://hal.archives-ouvertes.fr/hal-00844465
Contributor : Julien Dubois Connect in order to contact the contributor
Submitted on : Monday, July 15, 2013 - 11:54:53 AM
Last modification on : Friday, August 5, 2022 - 2:54:00 PM

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  • HAL Id : hal-00844465, version 1

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Imen Charfi, Johel Miteran, Julien Dubois, Mohamed Atri, Rached Tourki. Optimised spatio-temporal descriptors for real-time fall detection : comparison of SVM and Adaboost based classification. Journal of Electronic Imaging, SPIE and IS&T, 2013, Vol. 22 (issue 14), pp 17. ⟨hal-00844465⟩

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