Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
Smart Learning Institute, Beijing Normal University, Beijing 100875, China.
Math Biosci Eng. 2023 Apr 25;20(6):11238-11259. doi: 10.3934/mbe.2023498.
Accidental falls pose a significant threat to the elderly population, and accurate fall detection from surveillance videos can significantly reduce the negative impact of falls. Although most fall detection algorithms based on video deep learning focus on training and detecting human posture or key points in pictures or videos, we have found that the human pose-based model and key points-based model can complement each other to improve fall detection accuracy. In this paper, we propose a preposed attention capture mechanism for images that will be fed into the training network, and a fall detection model based on this mechanism. We accomplish this by fusing the human dynamic key point information with the original human posture image. We first propose the concept of dynamic key points to account for incomplete pose key point information in the fall state. We then introduce an attention expectation that predicates the original attention mechanism of the depth model by automatically labeling dynamic key points. Finally, the depth model trained with human dynamic key points is used to correct the detection errors of the depth model with raw human pose images. Our experiments on the Fall Detection Dataset and the UP-Fall Detection Dataset demonstrate that our proposed fall detection algorithm can effectively improve the accuracy of fall detection and provide better support for elderly care.
意外跌倒对老年人构成重大威胁，而准确地从监控视频中检测跌倒可以显著降低跌倒的负面影响。尽管大多数基于视频深度学习的跌倒检测算法都专注于训练和检测人体姿势或图片或视频中的关键点，但我们发现基于人体姿势的模型和基于关键点的模型可以相互补充，以提高跌倒检测的准确性。在本文中，我们提出了一种用于训练网络的图像预注意力捕获机制，以及一种基于该机制的跌倒检测模型。我们通过融合人体动态关键点信息和原始人体姿势图像来实现这一点。我们首先提出了动态关键点的概念，以解释跌倒状态下不完整的姿势关键点信息。然后，我们引入了一种注意力期望，通过自动标记动态关键点来预测深度模型的原始注意力机制。最后，使用带有人体动态关键点的深度模型来纠正带有原始人体姿势图像的深度模型的检测错误。我们在 Fall Detection Dataset 和 UP-Fall Detection Dataset 上的实验表明，我们提出的跌倒检测算法可以有效地提高跌倒检测的准确性，并为老年人护理提供更好的支持。
