# MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial and temporal structures in vehicle traffic

arXiv: https://arxiv.org/abs/2310.18548 · HTML: https://mlsanity.com/paper/2310.18548/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 28 Oct 2023.

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

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [Joint Detection and Tracking in Videos with Identification Features](https://mlsanity.com/paper/2005.10905/) (2020)
- [Artificial Intelligence and its Role in Near Future](https://mlsanity.com/paper/1804.01396/) (2018)
- [Good Practices and A Strong Baseline for Traffic Anomaly Detection](https://mlsanity.com/paper/2105.03827/) (2021)
- [Multiple-Vehicle Tracking in the Highway Using Appearance Model and Visual Object Tracking](https://mlsanity.com/paper/2006.07309/) (2020)

## Abstract (arXiv)

Currently, there are computer vision systems that help us with tasks that would be dull for humans, such as surveillance and vehicle tracking. An important part of this analysis is to identify traffic anomalies. An anomaly tells us that something unusual has happened, in this case on the highway. This paper aims to model vehicle tracking using computer vision to detect traffic anomalies on a highway. We develop the steps of detection, tracking, and analysis of traffic: the detection of vehicles from video of urban traffic, the tracking of vehicles using a bipartite graph and the Convex Hull algorithm to delimit moving areas. Finally for anomaly detection we use two data structures to detect the beginning and end of the anomaly. The first is the QuadTree that groups vehicles that are stopped for a long time on the road and the second that approaches vehicles that are occluded. Experimental results show that our method is acceptable on the Track4 test set, with an F1 score of 85.7% and a mean squared error of 25.432.