# Cut and Continuous Paste towards Real-time Deep Fall Detection Source: [Cut and Continuous Paste towards Real-time Deep Fall Detection]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:58:04.948Z Authors: Sunhee Hwang, Minsong Ki, Seung-Hyun Lee, Sanghoon Park, Byoung-Ki Jeon Published: 2022-02-22T06:07:16.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()