路上カメラによる車両位置推定と交通シーン再構成フレームワークCAROM
CAROM -- Vehicle Localization and Traffic Scene Reconstruction from Monocular Cameras on Road Infrastructures
路側の単眼監視カメラ映像から車両の種類・3D形状・位置・速度を推定し、交通シーンを再構成・再生するフレームワークを提案し、GPSやドローン映像を用いたベンチマークで平均0.8〜1.7mの精度を検証した。
著者: Duo Lu, Varun C Jammula, Steven Como, Jeffrey Wishart, Yan Chen, Yezhou Yang
分類: cs.RO
原文アブストラクト
Traffic monitoring cameras are powerful tools for traffic management and essential components of intelligent road infrastructure systems. In this paper, we present a vehicle localization and traffic scene reconstruction framework using these cameras, dubbed as CAROM, i.e., "CARs On the Map". CAROM processes traffic monitoring videos and converts them to anonymous data structures of vehicle type, 3D shape, position, and velocity for traffic scene reconstruction and replay. Through collaborating with a local department of transportation in the United States, we constructed a benchmarking dataset containing GPS data, roadside camera videos, and drone videos to validate the vehicle tracking results. On average, the localization error is approximately 0.8 m and 1.7 m within the range of 50 m and 120 m from the cameras, respectively.
