# Real-time Lane-wise Traffic Monitoring in Optimal ROIs

arXiv: https://arxiv.org/abs/2404.15212 · HTML: https://mlsanity.com/paper/2404.15212/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation](https://mlsanity.com/paper/2005.03572/) (2020)
- [City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones](https://mlsanity.com/paper/2105.06623/) (2021)
- [Artificial Intelligence Enabled Traffic Monitoring System](https://mlsanity.com/paper/2010.01217/) (2020)
- [Traffic Danger Recognition With Surveillance Cameras Without Training Data](https://mlsanity.com/paper/1811.11969/) (2018)

## Built on by (most cited first, 4 of 4)

- [Improving Generalization of Medical Image Registration Foundation Model](https://mlsanity.com/paper/2505.06527/) (2025)
- [Enhancing Vehicle Re-identification and Matching for Weaving Analysis](https://mlsanity.com/paper/2407.04688/) (2024)
- [Optimizing ROI Benefits Vehicle ReID in ITS](https://mlsanity.com/paper/2407.09966/) (2024)
- [Lane-Wise Highway Anomaly Detection](https://mlsanity.com/paper/2505.02613/) (2025)

## Abstract (arXiv)

In the US, thousands of Pan, Tilt, and Zoom (PTZ) traffic cameras monitor highway conditions. There is a great interest in using these highway cameras to gather valuable road traffic data to support traffic analysis and decision-making for highway safety and efficient traffic management. However, there are too many cameras for a few human traffic operators to effectively monitor, so a fully automated solution is desired. This paper introduces a novel system that learns the locations of highway lanes and traffic directions from these camera feeds automatically. It collects real-time, lane-specific traffic data continuously, even adjusting for changes in camera angle or zoom. This facilitates efficient traffic analysis, decision-making, and improved highway safety.