Pedestrian Detection Using Stationary Wavelet Dilated Residual Super-Resolution

Abstract

Pedestrian detection has become an important subject in applications such as automobile autopilot assistance and pedestrian tracking in surveillance systems. A number of powerful object detectors have been used in pedestrian detection to solve problems created by pedestrians’ posture, stature, clothing, viewing angle, shielding, and illumination; however, it is still a challenge to detect small-size and low-resolution (LR) images in pedestrian detection. In order to capture more scenes, surveillance cameras are usually set high above the ground, causing image sizes to be too small or the resolution too low. When these blurred or noisy images are imported into the object detector, the extraction of pedestrian feature information will lead to a relatively low detection rate. To solve this problem, this study proposes a novel stationary wavelet dilated residual super-resolution (SWDR-SR) network to greatly enhance the edge information of SR images and thereby improve pedestrian detection. The LR image is decomposed with stationary wavelet transform (SWT) into low- and high-frequency sub-images, which are then processed to restore the structure of low-frequency content and the details of high-frequency information so that the reconstructed SR image can also retain edge details, which is conducive to pedestrian detection. In addition, this study proposes a novel low-to-high frequency connection (L2HFC) to better retain edge details so as to further enhance pedestrian detection. Finally, you only look once version 4 (YOLOv4) is used for pedestrian detection on the SR images to validate the performance of the proposed method. Using seven common datasets, the experimental results indicate that the proposed SWDR-SR method is effective at detecting small-size and LR images.