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

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.