# Incident Detection on Junctions Using Image Processing

arXiv: https://arxiv.org/abs/2104.13437 · HTML: https://mlsanity.com/paper/2104.13437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 61 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving](https://mlsanity.com/paper/1905.01489/) (2019)
- [Joint Monocular 3D Vehicle Detection and Tracking](https://mlsanity.com/paper/1811.10742/) (2018)
- [Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation](https://mlsanity.com/paper/2010.03341/) (2020)
- [Fisheye Distortion Rectification from Deep Straight Lines](https://mlsanity.com/paper/2003.11386/) (2020)

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

- [Fast Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video using CNN and Bounding Box Propagation](https://mlsanity.com/paper/2207.01183/) (2022)

## Abstract (arXiv)

In traffic management, it is a very important issue to shorten the response time by detecting the incidents (accident, vehicle breakdown, an object falling on the road, etc.) and informing the corresponding personnel. In this study, an anomaly detection framework for road junctions is proposed. The final judgment is based on the trajectories followed by the vehicles. Trajectory information is provided by vehicle detection and tracking algorithms on visual data streamed from a fisheye camera. Deep learning algorithms are used for vehicle detection, and Kalman Filter is used for tracking. To observe the trajectories more accurately, the detected vehicle coordinates are transferred to the bird's eye view coordinates using the lens distortion model prediction algorithm. The system determines whether there is an abnormality in trajectories by comparing historical trajectory data and instantaneous incoming data. The proposed system has achieved 84.6% success in vehicle detection and 96.8% success in abnormality detection on synthetic data. The system also works with a 97.3% success rate in detecting abnormalities on real data.