Pedestrian Detection in Severe Weather Conditions
Paulius Tumas, Adam Nowosielski, Artūras Serackis
Abstract
Pedestrian detection has never been an easy task for computer vision and the automotive industry. Systems like the advanced driver-assistance system (ADAS) highly rely on far-infrared (FIR) data captured to detect pedestrians at nighttime. The recent development of deep learning-based detectors has proven the excellent results of pedestrian detection in perfect weather conditions. However, it is still unknown what the performance in adverse weather conditions is. In this paper, we introduce a 16-bit thermal data dataset called ZUT (Zachodniopomorski Uniwersytet Technologiczny) as having the widest variety of fine-grained annotated images captured in the four biggest European Union countries captured during severe weather conditions. We also provide a synchronized Controller Area Network (CAN bus) data, including driving speed, brake pedal status, and outside temperature for future ADAS system development. Furthermore, we have tested and provided 16-bit depth modifications for the YOLOv3 deep neural network (DNN) based detector, reaching a mean Average Precision (mAP) up to 89.1%. The ZUT dataset is published and publicly available at IEEE Dataport and Github.