Pedestrian detection has emerged as a fundamental technology for autonomous cars, robotics, pedestrian search, and other applications. Although many excellent object detection algorithms can be used for pedestrian detection, it is still a challenging problem due to the complicated real-world scenarios, e.g., the detection of pedestrians in low-quality surveillance videos. In this paper, we aim to study the challenging topic of pedestrian detection in low-quality images. Low-quality images are interpreted as those taken with a low-resolution camera, heavy weather or a blurred scene, making it difficult to distinguish pedestrians from the background. To solve this problem, we first introduce a dataset called playground (PG) for low-quality image detection. Images from PG are shot using two different camera views, and pedestrian images are taken at different periods, including day and night. The dataset contains a total of 5,752 images with 31,041 annotations. The average size of the pedestrian is $87\times 41$ and the image size is 480 $\times$ 640, indicating that these images are taken from very long distances. Then, we propose a super-resolution detection (SRD) network to enhance the resolution of low-quality images that can help distinguish pedestrians from the blurred background. Finally, based on these enhanced images, we adopt and improve the Faster R-CNN network to help relocate occluded pedestrians. Experimental results on this new dataset proved the efficiency and effectiveness of our algorithm on low-quality images.

行人检测已成为自动驾驶汽车，机器人技术，行人搜索和其他应用程序的基本技术。尽管许多优秀的对象检测算法可用于行人检测，但是由于复杂的现实世界场景（例如，低质量监控视频中的行人检测），它仍然是一个具有挑战性的问题。本文旨在研究低质量图像中行人检测的挑战性课题。低质量图像被解释为是使用低分辨率相机，恶劣天气或场景模糊所拍摄的图像，因此很难区分行人和背景。为了解决这个问题，我们首先引入了称为运动场（PG）的数据集，用于低质量图像检测。PG的图像是使用两种不同的相机视图拍摄的，行人图像是在不同的时间段拍摄的，包括白天和黑夜。数据集总共包含5,752张图像和31,041条注释。行人的平均人数为$87\times 41$ 图像尺寸为480 $\times$640，表示这些图像是从很远的距离拍摄的。然后，我们提出了一种超分辨率检测（SRD）网络，以增强低质量图像的分辨率，从而有助于将行人与模糊背景区分开。最后，基于这些增强的图像，我们采用并改进了Faster R-CNN网络，以帮助重新安置被遮挡的行人。在这个新数据集上的实验结果证明了我们的算法在低质量图像上的有效性和有效性。