Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey
It provides a comprehensive review for researchers and practitioners in computer vision and traffic monitoring, but is incremental as it builds on existing surveys.
This paper surveys visual surveillance research for anomaly detection on roads, summarizing key contributions from the last six years on features, techniques, and scenarios using single static cameras.
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.