Cut and Continuous Paste towards Real-time Deep Fall Detection
This work addresses fall detection for intelligent video surveillance systems, but it is incremental as it builds on existing deep learning approaches with a focus on efficiency.
The paper tackled real-time fall detection by proposing a simple convolutional neural network that simplifies the task to image classification using a new image synthesis method for motion representation, achieving satisfactory performance on URFD and AIHub airport datasets.
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.