  Deep learning based fall detection is one of the crucial tasks for
intelligent video surveillance systems, which aims to detect unintentional
falls of humans and alarm dangerous situations. In this work, we propose a
simple and efficient framework to detect falls through a single and small-sized
convolutional neural network. To this end, we first introduce a new image
synthesis method that represents human motion in a single frame. This
simplifies the fall detection task as an image classification task. Besides,
the proposed synthetic data generation method enables to generate a sufficient
amount of training dataset, resulting in satisfactory performance even with the
small model. At the inference step, we also represent real human motion in a
single image by estimating mean of input frames. In the experiment, we conduct
both qualitative and quantitative evaluations on URFD and AIHub airport
datasets to show the effectiveness of our method.
