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

## TL;DR

This survey reviews recent advances in anomaly detection in road traffic using visual surveillance, emphasizing learning methods, features, and scenarios with static cameras, and discusses future challenges and directions.

## Contribution

It provides a comprehensive overview of the last six years of research on anomaly detection in road traffic, focusing on learning techniques and visual features.

## Key findings

- Summarizes key learning methods used in anomaly detection.
- Highlights important features and scenarios in recent research.
- Discusses challenges and future research directions.

## Abstract

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.