Exemplar-based human body super-resolution for surveillance camera systems

In this paper, we propose an exemplar-based super-resolution method applied to a human body in a surveillance video. Since persons are usually captured as low-resolution images by a video surveillance system, it is sometimes necessary to perform detection and identification of persons from not only a human face but also from the human body appearance. The super-resolution for a human body image is difficult because the appearances of person images vary according to the color of clothing and the posture of persons. Thus, we focus on the high-frequency components that could restore the lost high-frequency components of the low-resolution image regardless to the variation of the clothing. Therefore, the purpose of the work presented in this paper is to apply the exemplar-based super-resolution using high-frequency components for a low-resolution human body image to generate a high-resolution human body image so that both computer systems and humans can identify persons more accurately. As a result of experiments, we confirmed the effectiveness of the proposed super-resolution method.

在本文中，我们提出了一种基于样本的超分辨率方法，该方法应用于监控视频中的人体。由于视频监控系统通常将人捕获为低分辨率图像，有时不仅需要从人脸，还需要从人体外观进行人员的检测和识别。人体图像的超分辨率是困难的，因为人物图像的外观会根据衣服的颜色和人的姿势而变化。因此，我们关注高频成分，这些高频成分能够恢复低分辨率图像丢失的高频成分，而不受衣服变化的影响。因此，本文所提出工作的目的是将基于高频成分的基于样本的超分辨率应用于低分辨率人体图像，以生成高分辨率人体图像，从而使计算机系统和人类都能更准确地识别人物。实验结果证实了所提出的超分辨率方法的有效性。