基于注意力机制子网络的时空跌倒检测算法

近年来行人跌倒检测变得越来越重要，因为准确及时的跌倒检测可以帮助跌倒者获得紧急救援。针对复杂场景中由于光照变化、遮挡和尺度变化等导致检测性能下降的问题，提出一种实时、鲁棒的跌倒检测算法。首先采用YOLO v3目标检测模块完成行人检测；然后在跟踪模块中对每个跟踪的边界框提取深层特征后，运用数据增强和重检测技术提高光照变化下的检测精度，并引入注意力机制子网络应对被遮挡目标的检测；最后跌倒判断模块对行人姿态进行判断，完成实时跌倒检测和报警。在Cityperson数据集、Montreal fall数据集和自建数据集上的实验结果表明，行人检测算法的检测精度达到87.05%，跌倒算法的检测精度达到98.55%，时延在120 ms以内，且在光照变化和遮挡影响下依然能获得良好的性能。

Spatio-temporal Fall Event Detection Algorithm Based on Attention Mechanism Subnetwork

In recent years, it has become increasingly important to automatically detect falls and provide feedback in emergency situations. In complex scenes, aiming at the problem of the degradation of detection performance due to changes in illumination, occlusion and scale changes, a real-time and robust fall detection algorithm is proposed. Firstly, the YOLO v3 target detection module is used to complete pedestrian detection. Then, after extracting the deep features of each tracked bounding box in the tracking module, data enhancement and re-detection techniques are used to improve the detection accuracy under light changes, and the attention mechanism subnetwork is introduced to deal with the detection of obscured targets. Finally, the final fall judgment module is used to judge the pedestrian posture and complete real-time fall detection and alarm. The experimental results on the Cityperson data set, Montreal fall data set and self-built data set show that the detection accuracy of the pedestrian detection algorithm reaches 87.05%, the detection accuracy of the fall algorithm reaches 98.55%, and the delay is within 120 ms. Good performance can still be obtained under the influence of occlusion.