Accurate fall detection for the assistance of older people is crucial to reduce incidents of deaths or injuries due to falls. Meanwhile, vision‐based fall detection system has shown some significant results to detect falls. Still, numerous challenges need to be resolved. The impact of deep learning has changed the landscape of the vision‐based system, such as action recognition. The deep learning technique has not been successfully implemented in vision‐based fall detection system due to the requirement of a large amount of computation power and requirement of a large amount of sample training data. This research aims to propose a vision‐based fall detection system that improves the accuracy of fall detection in some complex environments such as the change of light condition in the room. Also, this research aims to increase the performance of the pre‐processing of video images. The proposed system consists of Enhanced Dynamic Optical Flow technique that encodes the temporal data of optical flow videos by the method of rank pooling, which thereby improves the processing time of fall detection and improves the classification accuracy in dynamic lighting condition. The experimental results showed that the classification accuracy of the fall detection improved by around 3% and the processing time by 40–50 ms. The proposed system concentrates on decreasing the processing time of fall detection and improving the classification accuracy. Meanwhile, it provides a mechanism for summarizing a video into a single image by using dynamic optical flow technique, which helps to increase the performance of image preprocessing steps.

中文翻译：

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基于视觉的跌倒检测系统的深度学习：增强的光学动态流

准确的跌倒检测对老年人的帮助对于减少因跌倒造成的死亡或伤害事件至关重要。同时，基于视觉的跌倒检测系统已显示出一些可观的跌倒结果。尽管如此，仍需要解决许多挑战。深度学习的影响已经改变了基于视觉的系统的格局，例如动作识别。由于需要大量的计算能力和大量的样本训练数据，因此深度学习技术尚未在基于视觉的跌倒检测系统中成功实现。这项研究旨在提出一种基于视觉的跌倒检测系统，该系统可以提高某些复杂环境（例如室内光线条件的变化）中跌倒检测的准确性。还，这项研究旨在提高视频图像预处理的性能。所提出的系统由增强动态光流技术组成，该技术利用秩合并方法对光流视频的时间数据进行编码，从而缩短了跌倒检测的处理时间，并提高了动态光照条件下的分类精度。实验结果表明，跌倒检测的分类精度提高了约3％，处理时间提高了40–50 ms。提出的系统集中在减少跌倒检测的处理时间和提高分类精度上。同时，它提供了一种通过使用动态光流技术将视频汇总为单个图像的机制，这有助于提高图像预处理步骤的性能。