Detecting coherent groups is fundamentally important for crowd behavior analysis. In the
past few decades, plenty of works have been conducted on this topic, but most of them have …

Group detection is fundamentally important for analyzing crowd behaviors, and has attracted
plenty of attention in artificial intelligence. However, existing works mostly have limitations …

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 …

Automatically detecting events in crowded scenes is a challenging task in Computer Vision.
A number of offline approaches have been proposed for solving the problem of crowd …

Crowd understanding has drawn increasing attention from the computer vision community,
and its progress is driven by the availability of public crowd datasets. In this paper, we …

Modern crowd theories agree that collective behavior is the result of the underlying
interactions among small groups of individuals. In this work, we propose a novel algorithm …

In this work, we propose an unsupervised approach for crowd scene anomaly detection and
localization using a social network model. Using a window-based approach, a video scene …

People in crowd scenes always exhibit consistent behaviors and form collective motions.
The analysis of collective motion has motivated a surge of interest in computer vision …

Groups are the primary entities that make up a crowd. Understanding group-level dynamics
and properties is thus scientifically important and practically useful in a wide range of …

The crowd distribution information is the crucial information for abnormal behaviors
detection in the crowd scenes. In this paper, we firstly refer to the definition of the entropy …