Fall Vision: A Benchmark Video Dataset for Advancing Fall Detection Technology

In this paper, an exhaustive video dataset categorized as fall and no-fall videos is presented, which was compiled for the specific purpose of fall detection research. The dataset comprises three fundamental classifications of falls, namely those originating from a standing position, bed, or chair. After being initially acquired in unprocessed form, these videos underwent subsequent processing to generate seminal videos, which were presented with and without a black backdrop.
The dataset was obtained from voluntary participants through the use of handheld devices (e.g., digital cameras or mobile phones), which ensured ethical compliance and informed assent. The dataset provides a substantial asset for the progression of fall detection algorithms, serving as a resilient framework for the development and evaluation of such algorithms.
The implementation of fall detection systems is critical, especially in situations involving elderly individuals who occur during medical emergencies that lead to falls and require immediate assistance, or when individuals are solitary and unable to restore their balance after falling. By utilizing this dataset, scientists have the opportunity to investigate a wide range of methodologies, such as deep learning and computer vision, in order to develop and enhance fall detection systems. This video dataset has the potential to contribute to the development of fall detection technology, thereby improving safety protocols for vulnerable populations, due to its availability to researchers.

搜集汇总

背景与挑战

背景概述

该数据集是一个用于跌倒检测研究的基准视频数据集，包含跌倒和非跌倒视频，并基于站立、床和椅子三种跌倒场景进行分类。视频经过处理并提供有/无黑色背景的版本，通过手持设备从自愿参与者收集，旨在支持机器学习和计算机视觉算法的开发与评估，以提升对老年人等脆弱人群的安全保护。