Pose estimation at night in infrared images using a lightweight multi-stage attention network

Human Keypoints Detection is a relatively basic task in computer vision; it is the pre-task of human action recognition, behavior analysis and human-computer interaction. Since most abnormal actions occur at night, how to effectively extract skeleton sequence data in a low-light or completely dark environment poses a huge challenge for its identification. This paper proposes to use far infrared images to detection key points of the human body, which can solve the problem of human pose estimation under challenging weather conditions such as total darkness, smoke, inclement weather and glare. However, far-infrared images have some shortcomings, such as low resolution, noise and thermal characteristics; the skeleton data need to be provided in real time for the next stage of task. Based on the above reasons, this paper proposes a lightweight multi-stage attention network (LMANet) to detect the key points of human at night. This new network structure adds context information through the large receptive field, which helps to assist the detection of neighboring key points through this information, but for the sake of lightweight consideration, this article only extends the network to two stages. In addition, this article uses the attention module to effectively select channels with a large amount of information and highlight the features of key points, while eliminating background interference. In order to detect key points of the human in various complex environments, we use techniques such as difficult sample mining which improves the accuracy of key points with low confidence. Our network has been verified on two visible light datasets, fully demonstrating excellent performance. This paper successfully introduces far-infrared images into the field of pose estimation, because there is no public dataset for far-infrared pose estimation. In this paper, 700 images are selected for annotation from multiple public far-infrared object detection, segmentation and action recognition datasets; our algorithm is verified on this dataset; the effect is very good. After the paper is published, we will publish our key points of the human body annotated documents.