Identifying High Accuracy Regions in Traffic Camera Images to Enhance the Estimation of Road Traffic Metrics: A Quadtree-Based Method

TL;DR

This paper introduces a quadtree-based algorithm to identify high-accuracy regions in traffic camera images, significantly improving vehicle detection and traffic density estimation across varied camera conditions.

Contribution

The paper presents a novel quadtree-based method to isolate high-accuracy detection regions, enhancing traffic metric reliability from diverse camera feeds.

Findings

Vehicle detection accuracy increased by 41% within HAIR.

Traffic density estimation error decreased by 49%.

Method effective across different camera heights and resolutions.

Abstract

The growing number of real-time camera feeds in urban areas has made it possible to provide high-quality traffic data for effective transportation planning, operations, and management. However, deriving reliable traffic metrics from these camera feeds has been a challenge due to the limitations of current vehicle detection techniques, as well as the various camera conditions such as height and resolution. In this work, a quadtree based algorithm is developed to continuously partition the image extent until only regions with high detection accuracy are remained. These regions are referred to as the high-accuracy identification regions (HAIR) in this paper. We demonstrate how the use of the HAIR can improve the accuracy of traffic density estimates using images from traffic cameras at different heights and resolutions in Central Ohio. Our experiments show that the proposed algorithm can be used to derive robust HAIR where vehicle detection accuracy is 41 percent higher than that in the original image extent. The use of the HAIR also significantly improves the traffic density estimation with an overall decrease of 49 percent in root mean squared error.