Turning traffic surveillance cameras into intelligent sensors for traffic density estimation

Abstract

Accurate traffic density plays a pivotal role in the Intelligent Transportation Systems (ITS). The current practice to obtain the traffic density is through specialized sensors. However, those sensors are placed in limited locations due to the cost of installation and maintenance. In most metropolitan areas, traffic surveillance cameras are widespread in road networks, and they are the potential data sources for estimating traffic density in the whole city. Unfortunately, such an application is challenging since surveillance cameras are affected by the 4L characteristics: Low frame rate, Low resolution, Lack of annotated data, and Located in complex road environments. To the best of our knowledge, there is a lack of holistic frameworks for estimating traffic density from traffic surveillance camera data with 4 L characteristics. Therefore, we propose a framework for estimating traffic density using uncalibrated traffic surveillance cameras. 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) for the estimated traffic density is 9.04 veh/km/lane in Hong Kong and 7.03 veh/km/lane in Sacramento. The research outcomes can provide accurate traffic density without installing additional sensors.