Fall Detection in Video Sequences Based on a Three-Stream Convolutional Neural Network

摘要

Human falls are more susceptible in advanced ages and the second most common cause of accidental death to elders, because of the later detecting falls became a crucial research topic to an aging population. To this effect, we propose and evaluate the employment of a multi-stream approach to detect fall events. Three features (optical flow, saliency map, and RGB data) fed to each stream of a VGG-16 and classified by an SVM of whether there was or not a fall event. Experiments are conducted on two datasets, URFD and FDD, achieving accuracy rates of 98.84% and 99.51%, respectively, which outperforms the majority of the reviewed solutions.