Turning Traffic Monitoring Cameras into Intelligent Sensors for Traffic Density Estimation

Abstract

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.

I see you: A Vehicle-Pedestrian Interaction Dataset from Traffic Surveillance Cameras

“…Every video has daytime, sunset and nighttime scenes these variations in illumination lead us to fine-tune the YOLOv5 models used for detection. For each category (vehicle, pedestrian) we used a different pre-trained YOLOv5 model, the vehicle category is detected by the model used in [15] and the pedestrian category is detected by a model pre-trained in the COCO dataset. To capture all illumination variations we sampled the 18-hour videos saving a frame every 6 minutes resulting in 4246 images in total.…”

Novel Optimization Approach for Camera Calibration in Traffic Scenes

“…Then, actual physical properties of objects can be obtained. In intelligent transportation systems (ITS), camera calibration can be used for vehicle speed estimation, 3D vehicle structure recognition, and traffic flow statistical analysis, which are of great practical value for traffic management and control (1)(2)(3)(4)(5).…”