  Accurate traffic state information plays a pivotal role in the Intelligent
Transportation Systems (ITS), and it is an essential input to various smart
mobility applications such as signal coordination and traffic flow prediction.
The current practice to obtain the traffic state information is through
specialized sensors such as loop detectors and speed cameras. In most
metropolitan areas, traffic monitoring cameras have been installed to monitor
the traffic conditions on arterial roads and expressways, and the collected
videos or images are mainly used for visual inspection by traffic engineers.
Unfortunately, the data collected from traffic monitoring cameras are affected
by the 4L characteristics: Low frame rate, Low resolution, Lack of annotated
data, and Located in complex road environments. Therefore, despite the great
potentials of the traffic monitoring cameras, the 4L characteristics hinder
them from providing useful traffic state information (e.g., speed, flow,
density). This paper focuses on the traffic density estimation problem as it is
widely applicable to various traffic surveillance systems. To the best of our
knowledge, there is a lack of the holistic framework for addressing the 4L
characteristics and extracting the traffic density information from traffic
monitoring camera data. In view of this, this paper proposes a framework for
estimating traffic density using uncalibrated traffic monitoring cameras with
4L characteristics. The proposed framework consists of two major components:
camera calibration and vehicle detection. The camera calibration method
estimates the actual length between pixels in the images and videos, and the
vehicle counts are extracted from the deep-learning-based vehicle detection
method. Combining the two components, high-granular traffic density can be
estimated. To validate the proposed framework, two case studies were conducted
in Hong Kong and Sacramento. The results show that the Mean Absolute Error
(MAE) in camera calibration is less than 0.2 meters out of 6 meters, and the
accuracy of vehicle detection under various conditions is approximately 90%.
Overall, the MAE for the estimated density is 9.04 veh/km/lane in Hong Kong and
1.30 veh/km/lane in Sacramento. The research outcomes can be used to calibrate
the speed-density fundamental diagrams, and the proposed framework can provide
accurate and real-time traffic information without installing additional
sensors.
