Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey

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.

References in corpus (5)

• Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks
• Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
• Review on Computer Vision Techniques in Emergency Situation
• A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories
• Temporal Unknown Incremental Clustering (TUIC) Model for Analysis of Traffic Surveillance Videos