Human posture estimation at low resolution

Human pose estimation is an important research direction in the field of computer vision, which is widely used in human-computer interaction, behavior analysis, and intelligent surveillance. Although existing human pose estimation algorithms possess high accuracy for high-resolution images, they perform poorly for low-resolution images that are prevalent in practical applications, and thus are difficult to be widely used in people's daily lives. In this paper, we propose a new end-to-end network framework for accurate human pose estimation of low-resolution images by combining super-resolution assistance and quantization error optimization. In addition, a composite loss function is designed to jointly train the super-resolution network to generate high-resolution images that contribute to human pose estimation instead of simple pre-processing. The experimental results show that the mAP of our method reaches 68.1% and 61.4% on the COCO datasets downsampled to 128×96 and 64×48.