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

arXiv: https://arxiv.org/abs/2106.14049 · HTML: https://mlsanity.com/paper/2106.14049/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 59 references that are in the map.
- First posted on arXiv 26 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [Deep Learning vs. Traditional Computer Vision](https://mlsanity.com/paper/1910.13796/) (2019)
- [How Far are We from Solving Pedestrian Detection?](https://mlsanity.com/paper/1602.01237/) (2016)
- [Global visual localization in LiDAR-maps through shared 2D-3D embedding space](https://mlsanity.com/paper/1910.04871/) (2019)
- [Do We Need More Training Data?](https://mlsanity.com/paper/1503.01508/) (2015)
- [Understanding Traffic Density from Large-Scale Web Camera Data](https://mlsanity.com/paper/1703.05868/) (2017)

## Built on by (most cited first, 1 of 1)

- [Optimizing ROI Benefits Vehicle ReID in ITS](https://mlsanity.com/paper/2407.09966/) (2024)

## Abstract (arXiv)

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.