# Geolocation estimation of target vehicles using image processing and geometric computation

arXiv: https://arxiv.org/abs/2203.10938 · HTML: https://mlsanity.com/paper/2203.10938/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 3 of 3)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera](https://mlsanity.com/paper/2005.01632/) (2020)

## Built on by (most cited first, 3 of 3)

- [Geo-locating Road Objects using Inverse Haversine Formula with NVIDIA Driveworks](https://mlsanity.com/paper/2401.07582/) (2024)
- [Framework and Multi-modal Dataset for Roadwork Zone Detection and Geo-localization](https://mlsanity.com/paper/2607.04330/) (2026)
- [A Practical Large-Scale Roadside Multi-View Multi-Sensor Spatial Synchronization Framework for Intelligent Transportation Systems](https://mlsanity.com/paper/2311.04231/) (2023)

## Abstract (arXiv)

Estimating vehicles' locations is one of the key components in intelligent traffic management systems (ITMSs) for increasing traffic scene awareness. Traditionally, stationary sensors have been employed in this regard. The development of advanced sensing and communication technologies on modern vehicles (MVs) makes it feasible to use such vehicles as mobile sensors to estimate the traffic data of observed vehicles. This study aims to explore the capabilities of a monocular camera mounted on an MV in order to estimate the geolocation of the observed vehicle in a global positioning system (GPS) coordinate system. We proposed a new methodology by integrating deep learning, image processing, and geometric computation to address the observed-vehicle localization problem. To evaluate our proposed methodology, we developed new algorithms and tested them using real-world traffic data. The results indicated that our proposed methodology and algorithms could effectively estimate the observed vehicle's latitude and longitude dynamically.