Abstract: 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.