Recognizing human activities in their daily living enables the development and widely
usage of human-centric applications, such as health monitoring, assisted living, etc …

We present a novel unsupervised deep learning framework for anomalous event detection
in complex video scenes. While most existing works merely use hand-crafted appearance …

Crowded scene understanding is a fundamental problem in computer vision. In this study,
we develop a multi-task deep model to jointly learn and combine appearance and motion …

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 …

This paper presents a hierarchical framework for detecting local and global anomalies via
hierarchical feature representation and Gaussian process regression. While local anomaly …

In this paper, we propose a method for real-time anomaly detection and localization in
crowded scenes. Each video is defined as a set of non-overlapping cubic patches, and is …

We introduce a novel, online method to predict pedestrian trajectories using agent-based
velocity-space reasoning for improved human–robot interaction and collision-free …

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 …

Computer simulations are increasingly used to monitor and predict behavior at large crowd
events, such as mass gatherings, festivals and evacuations. We critically examine the crowd …