Significantly improving human detection in low-resolution images by retraining YOLOv3

Abstract

Human detection in images is a crucial task due to its usage in different areas including person detection and identification, abnormal surveillance and crowd counting. Low-resolution of image sequences taken by stationary outdoor surveillance cameras is very challenging. Detecting human with deep learning techniques, is more powerful than traditional methods due to its ability to learn high-level deeper features, high detection accuracy and speed. Therefore, this paper proposes a method for human detection in low-resolution images based on YOLOv3. This method will prepare a dataset of low-resolution images collected by outdoor surveillance cameras and annotate them manually. Next, we retrain YOLOv3 to make an improved model for low-resolution images. The model achieves F1-score of 0.804 human detecting for low-resolution test images.