Pedestrian detection with super-resolution reconstruction for low-quality image

Abstract

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 87×41 and the image size is 480 × 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.