  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.
