Crowded Scene Analysis: A Survey

TL;DR

This survey reviews recent advancements in crowded scene analysis, covering techniques for motion, behavior, and anomaly detection, highlighting challenges, datasets, and future research directions in the field.

Contribution

It provides a comprehensive overview of current methods, datasets, and evaluation protocols in crowded scene analysis, identifying key challenges and future research opportunities.

Findings

Summarizes state-of-the-art techniques in crowd motion and behavior analysis.

Details available datasets and evaluation metrics for crowded scene analysis.

Discusses open challenges and promising future research directions.

Abstract

Automated scene analysis has been a topic of great interest in computer vision and cognitive science. Recently, with the growth of crowd phenomena in the real world, crowded scene analysis has attracted much attention. However, the visual occlusions and ambiguities in crowded scenes, as well as the complex behaviors and scene semantics, make the analysis a challenging task. In the past few years, an increasing number of works on crowded scene analysis have been reported, covering different aspects including crowd motion pattern learning, crowd behavior and activity analysis, and anomaly detection in crowds. This paper surveys the state-of-the-art techniques on this topic. We first provide the background knowledge and the available features related to crowded scenes. Then, existing models, popular algorithms, evaluation protocols, as well as system performance are provided corresponding to different aspects of crowded scene analysis. We also outline the available datasets for performance evaluation. Finally, some research problems and promising future directions are presented with discussions.