# Vehicle-counting with Automatic Region-of-Interest and Driving-Trajectory detection

arXiv: https://arxiv.org/abs/2108.07135 · HTML: https://mlsanity.com/paper/2108.07135/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 16 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [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)

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

- [Automated Visual Attention Detection using Mobile Eye Tracking in Behavioral Classroom Studies](https://mlsanity.com/paper/2505.07552/) (2025)
- [Optimizing ROI Benefits Vehicle ReID in ITS](https://mlsanity.com/paper/2407.09966/) (2024)
- [Intelligent ROI-Based Vehicle Counting Framework for Automated Traffic Monitoring](https://mlsanity.com/paper/2604.12470/) (2026)

## Abstract (arXiv)

Vehicle counting systems can help with vehicle analysis and traffic incident detection. Unfortunately, most existing methods require some level of human input to identify the Region of interest (ROI), movements of interest, or to establish a reference point or line to count vehicles from traffic cameras. This work introduces a method to count vehicles from traffic videos that automatically identifies the ROI for the camera, as well as the driving trajectories of the vehicles. This makes the method feasible to use with Pan-Tilt-Zoom cameras, which are frequently used in developing countries. Preliminary results indicate that the proposed method achieves an average intersection over the union of 57.05% for the ROI and a mean absolute error of just 17.44% at counting vehicles of the traffic video cameras tested.