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.
翻译:基于深层学习的秋天探测是智能视频监视系统的关键任务之一,该系统旨在探测人类无意的坠落和警报危险情况。在这项工作中,我们提出了一个简单而有效的框架,通过单一和小型的进化神经网络探测坠落。为此,我们首先采用一个新的图像合成方法,在单一的框架中代表人类运动。这简化了秋天探测任务,作为图像分类任务。此外,拟议的合成数据生成方法能够产生足够数量的训练数据集,导致即使使用小模型也能产生令人满意的性能。在推断步骤中,我们还通过估计输入框架的平均值,在单一图像中代表真正的人类运动。在实验中,我们对URFD和AIHub机场数据集进行定性和定量评价,以显示我们方法的有效性。