Robot navigation in crowded public spaces is a complex task that requires addressing a
variety of engineering and human factors challenges. These challenges have motivated a …

In group activity recognition, the temporal dynamics of the whole activity can be inferred
based on the dynamics of the individual people representing the activity. We build a deep …

Pedestrian behavior modeling and analysis is important for crowd scene understanding and
has various applications in video surveillance. Stationary crowd groups are a key factor …

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 …

Collective motions are common in crowd systems and have attracted a great deal of
attention in a variety of multidisciplinary fields. Collectiveness, which indicates the degree of …

In this paper, a deep neural network (Behavior-CNN) is proposed to model pedestrian
behaviors in crowded scenes, which has many applications in surveillance. A pedestrian …

Collective behaviors characterize the intrinsic dynamics of the crowds. Automatically
understanding collective crowd behaviors has important applications to video surveillance …

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 …

The local feature based approaches have become popular for activity recognition. A local
feature captures the local movement and appearance of a local region in a video, and thus …

To obtain a more comprehensive activity understanding for a crowded scene, in this paper,
we propose a new problem of panoramic human activity recognition (PAR), which aims to …