Recent trends in crowd analysis: A review

Abstract

When overpopulated cities face frequent crowded events like strikes, demonstrations, parades or other sorts of people gatherings, they are confronted to multiple security issues. To mitigate these issues, security forces are often involved to monitor the gatherings and to ensure the security of their participants. However, when access to technology is limited, the security forces can quickly become overwhelmed. Fortunately, more and more important smart cities are adopting the concept of intelligent surveillance systems. In these situations, intelligent surveillance systems require the most advanced techniques of crowd analysis to monitor crowd events properly. In this review, we explore various studies related to crowd analysis. Crowd analysis is commonly broken down into two major branches: crowd statistics and crowd behavior analysis. When crowd statistics determines the Level Of Service (LoS) of a crowded scene, crowd behavior analysis describes the motion patterns and the activities that are observed in a scene. One of the hottest topics of crowd analysis is anomaly detection. Although a unanimous definition of anomaly has not yet been met, each of crowd analysis subtopics can be subjected to abnormality. The purpose of our review is to find subareas, in crowd analysis, that are still unexplored or that seem to be rarely addressed through the prism of Deep Learning.