一种基于关键点的红外图像人体摔倒检测方法

针对已有人体摔倒检测方法在复杂环境场景下易受光照影响、适应性差、误检率高等问题，提出了一种基于关键点估计的红外图像人体摔倒检测方法。该方法采用红外图像，有效避免了光照等因素的影响，经过神经网络找到人体目标中心点，然后回归人体目标属性，如目标尺寸、标签等，从而得到检测结果。使用红外相机采集不同情况下的人体摔倒图像，建立红外图像人体摔倒数据集并使用提出的方法进行检测，识别率达到97%以上。实验结果表明提出的方法在红外图像人体摔倒检测中具有较高的精度与速度。

Human Fall Detection Method Based on Key Points in Infrared Images

To address the problems with existing human fall detection methods for complex environments, which are susceptible to light, poor adaptability, and high false detection rates, an infrared image human fall detection method based on key point estimation is proposed. This method uses infrared images, which effectively eliminates the influence of factors such as lighting; first, the center point of the human target is found through a neural network, and second, the human target attributes, such as the target size and label, are regressed to obtain detection results. An infrared camera was used to collect human body fall images in different situations and establish datasets containing infrared images of human falls. The proposed method was used for experiments; the recognition rate exceeded 97%. The experimental results show that the proposed method has a higher accuracy and speed than other two methods in infrared image human fall detection.