# Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey

arXiv: https://arxiv.org/abs/1901.08292 · HTML: https://mlsanity.com/paper/1901.08292/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 30 of 30)

- [Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models](https://mlsanity.com/paper/2302.05087/) (2023)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [Deep Learning for Video Anomaly Detection: A Review](https://mlsanity.com/paper/2409.05383/) (2024)
- [A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees](https://mlsanity.com/paper/2104.06856/) (2021)
- [Networking Systems for Video Anomaly Detection: A Tutorial and Survey](https://mlsanity.com/paper/2405.10347/) (2024)
- [Continuous Memory Representation for Anomaly Detection](https://mlsanity.com/paper/2402.18293/) (2024)
- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [Privacy-Preserving Video Anomaly Detection: A Survey](https://mlsanity.com/paper/2411.14565/) (2024)
- [Survey on video anomaly detection in dynamic scenes with moving cameras](https://mlsanity.com/paper/2308.07050/) (2023)
- [FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection](https://mlsanity.com/paper/2406.15283/) (2024)
- [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://mlsanity.com/paper/2211.05120/) (2022)
- [Anomaly Detection using Edge Computing in Video Surveillance System: Review](https://mlsanity.com/paper/2107.02778/) (2021)
- [Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey](https://mlsanity.com/paper/2412.00348/) (2024)
- [A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment](https://mlsanity.com/paper/2508.14203/) (2025)
- [Towards Continual Visual Anomaly Detection in the Medical Domain](https://mlsanity.com/paper/2508.18013/) (2025)
- [From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management](https://mlsanity.com/paper/2304.03368/) (2023)
- [Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning](https://mlsanity.com/paper/2312.04398/) (2023)
- [Anomaly Detection in Residential Video Surveillance on Edge Devices in IoT Framework](https://mlsanity.com/paper/2107.04767/) (2021)
- [Contextual Information Based Anomaly Detection for a Multi-Scene UAV Aerial Videos](https://mlsanity.com/paper/2203.15437/) (2022)
- [Spatio-Temporal Relation Learning for Video Anomaly Detection](https://mlsanity.com/paper/2209.13116/) (2022)
- [Pedestrian Spatio-Temporal Information Fusion For Video Anomaly Detection](https://mlsanity.com/paper/2211.10052/) (2022)
- [Uncertainty Quantification for Image-based Traffic Prediction across Cities](https://mlsanity.com/paper/2308.06129/) (2023)
- [uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories](https://mlsanity.com/paper/2404.12712/) (2024)
- [Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving](https://mlsanity.com/paper/2406.06423/) (2024)
- [AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties](https://mlsanity.com/paper/2410.24178/) (2024)
- [Integrating Generative Adversarial Networks and Convolutional Neural Networks for Enhanced Traffic Accidents Detection and Analysis](https://mlsanity.com/paper/2506.16186/) (2025)
- [What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies](https://mlsanity.com/paper/2507.06513/) (2025)
- [Zoom In, Reason Out: Efficient Far-field Anomaly Detection in Expressway Surveillance Videos via Focused VLM Reasoning Guided by Bayesian Inference](https://mlsanity.com/paper/2604.23724/) (2026)
- [An Analysis Focused on Womens Safety: Can VAD Models Be Enhanced by a Multi-modal Dataset?](https://mlsanity.com/paper/2605.25806/) (2026)

## Abstract (arXiv)

Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. In this paper, we present a survey on relevant visual surveillance related researches for anomaly detection in public places, focusing primarily on roads. Firstly, we revisit the surveys done in the last 10 years in this field. Since the underlying building block of a typical anomaly detection is learning, we emphasize more on learning methods applied on video scenes. We then summarize the important contributions made during last six years on anomaly detection primarily focusing on features, underlying techniques, applied scenarios and types of anomalies using single static camera. Finally, we discuss the challenges in the computer vision related anomaly detection techniques and some of the important future possibilities.