Accurate Localization of Road Traffic Objects on the Road Plane Using Surveillance Camera Imagery

Abstract

Accurate vehicle localization from monocular roadside surveillance cameras is an important problem in intelligent transportation systems, traffic monitoring, and traffic conflict analysis. Standard localization approaches typically estimate vehicle position using the center of the detector bounding box, which may lead to large localization errors due to perspective distortion and parallax effects, particularly for elevated roadside cameras and large vehicles.This paper proposes a two-stage geometry-aware localization pipeline that estimates the projection of the vehicle footprint onto the road plane instead of relying directly on detector geometry. In the first stage, vehicles are detected using a YOLO26-based detector. In the second stage, a dedicated ResNet34 regression network predicts four corner points corresponding to the projection of the vehicle base onto the image plane. The final vehicle position is estimated as the geometric center of the predicted quadrilateral.The proposed method was trained using synthetic data generated in the CARLA simulation environment and subsequently fine-tuned on real-world roadside imagery from the DAIR-V2X dataset. Experimental evaluation performed on both synthetic and real-world data demonstrated clear improvements in localization accuracy compared with naive bounding-box-center-based localization. On the DAIR-V2X dataset, the proposed approach reduced the mean image-space localization error from 31.77 px to 15.30 px (51.8% improvement) and the median error to 4.29 px. Median ground-plane localization error for medium-range vehicles decreased from 5.52 m to 0.90 m, while for far-range vehicles it decreased from 8.67 m to 1.84 m.The experiments additionally demonstrated that contextual information surrounding the detector bounding box plays an important role in geometric localization. The largest improvements were observed for distant vehicles and geometrically challenging cases affected by strong perspective distortion and parallax effects.