# Anomalous Motion Detection on Highway Using Deep Learning

arXiv: https://arxiv.org/abs/2006.08143 · HTML: https://mlsanity.com/paper/2006.08143/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

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

- [TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance](https://mlsanity.com/paper/2209.12386/) (2022)
- [Survey on video anomaly detection in dynamic scenes with moving cameras](https://mlsanity.com/paper/2308.07050/) (2023)
- [Multimedia Datasets for Anomaly Detection: A Review](https://mlsanity.com/paper/2112.05410/) (2021)
- [An aerial color image anomaly dataset for search missions in complex forested terrain](https://mlsanity.com/paper/2507.15492/) (2025)

## Abstract (arXiv)

Research in visual anomaly detection draws much interest due to its applications in surveillance. Common datasets for evaluation are constructed using a stationary camera overlooking a region of interest. Previous research has shown promising results in detecting spatial as well as temporal anomalies in these settings. The advent of self-driving cars provides an opportunity to apply visual anomaly detection in a more dynamic application yet no dataset exists in this type of environment. This paper presents a new anomaly detection dataset - the Highway Traffic Anomaly (HTA) dataset - for the problem of detecting anomalous traffic patterns from dash cam videos of vehicles on highways. We evaluate state-of-the-art deep learning anomaly detection models and propose novel variations to these methods. Our results show that state-of-the-art models built for settings with a stationary camera do not translate well to a more dynamic environment. The proposed variations to these SoTA methods show promising results on the new HTA dataset.