A simple and effective multi-person pose estimation model for low power embedded system

In recent years, algorithms based on human pose estimation have been applied more and more in low power embedded system. However, the keypoints detection under occlusion is not well solved, resulting in poor effect in practical application on embedded devices. In this paper, we propose a novel Simple and Effective Network (SEN) to deal with the multi-person pose estimation problem of low power embedded system via detecting occlusion keypoints quite well to a certain extent. This model is easy to apply to embedded devices, and has the characteristics of simplicity, strong expansibility and wide application, which are very important in the world of Internet of things. Our model contains three novel modules: Feature Fusion Module (FFM), Channel Enhancement Attention Module (CEAM), and Feature Enhancement Module (FEM). The FFM fuses the shallow and deep feature maps, bringing rich context information to the model. At the same time, it can alleviate the problem of information loss caused by downsampling operations and locate the keypoints more accurately. The FEM and the CEAM act on the deep feature maps of the network, which helps to infer the keypoints of occlusion or invisibility. Related experiments explain that the raised means is effective and achieves the superior performance over two benchmark datasets: the COCO keypoints detection dataset and the MPII Human Pose dataset.