Fall Vision: A Benchmark Video Dataset for Advancing Fall Detection Technology
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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.
本文提出了一类专为跌倒检测研究编制的详尽视频数据集，该数据集分为跌倒与非跌倒视频两类。数据集涵盖三类基础跌倒场景，分别为源自站立位、床或椅子的跌倒行为。这些视频最初以未处理格式采集，随后经过处理生成标准视频，分别带有黑色背景与无黑色背景两种呈现形式。 本数据集通过手持设备（如数码相机或移动电话）从自愿参与者处采集，确保研究符合伦理规范且获得了知情同意。该数据集为跌倒检测算法的迭代优化提供了宝贵资源，可作为此类算法开发与评估的稳健基准框架。 跌倒检测系统的部署至关重要，尤其适用于老年群体在医疗紧急状况下发生跌倒且需即时救助的场景，或是个体独处时跌倒后无法自行恢复平衡的情形。借助该数据集，研究人员可探索深度学习、计算机视觉等多种技术方法，以开发并优化跌倒检测系统。由于该数据集可供研究人员获取，其有望推动跌倒检测技术的发展，从而提升脆弱群体的安全保障水平。
