Machine learning in video surveillance for fall detection

Abstract

The present paper considers the usage of deep learning and transfers learning techniques in fall detection by means of surveillance camera data processing. As a dataset, an open dataset gathered by the Laboratory of Electronics and Imaging of the National Center for Scientific Research in Chalon-sur-Saone was used. The architecture of the CNN AlexNet, which was used as a starting point for the classifier, was adapted to solve fall detection problem. The proposed method was tested on a dataset of 30 records containing a single fall episode each. We achieved Cohen's kappa of 0.93 and 0.60 for the fall — non-fall classification for the known and unknown for classifier surrounding conditions, respectively.