Abstract: Falls in crowded places are easy to cause safety problems, and real-time monitoring can reduce safety risks. Aiming at the problems of large scale and poor timeliness of existing fall detection methods based on pose estimation, this paper proposes an OpenPose human fall detection algorithm DSC OpenPose that integrates attention mechanism. Drawing on DenseNet’s dense connection idea, each layer is directly connected with all previous layers in the channel dimension to achieve feature reuse and reduce the scale of model parameters; A method of identifying fall behavior based on the outer ellipse parameters, head height and lower limb height is proposed to realize the fall detection of human objects. The experimental results on COCO dataset show that compared with other algorithms, this algorithm achieves a good balance between model size and accuracy. At the same time, the fall detection method proposed in this paper has an accuracy  of 98.2%, an precision of 96.6%, and a detection speed of 20.2 frame/s on the RF data set, and the model scale is small to meet the real-time reasoning needs of embedded devices.