With the increasing number of surveillance cameras in both indoor and outdoor locations,
there is a grown demand for an intelligent system that detects abnormal events. Although …

Understanding human motion behavior is critical for autonomous moving platforms (like self-
driving cars and social robots) if they are to navigate human-centric environments. This is …

Surveillance videos are able to capture a variety of realistic anomalies. In this paper, we
propose to learn anomalies by exploiting both normal and anomalous videos. To avoid …

Robots that navigate through human crowds need to be able to plan safe, efficient, and
human predictable trajectories. This is a particularly challenging problem as it requires the …

The detection of abnormal behaviour in crowded scenes has to deal with many challenges.
This paper presents an efficient method for detection and localization of anomalies in …

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 …

Pedestrian trajectory prediction is a challenging task because of the complex nature of
humans. In this paper, we tackle the problem within a deep learning framework by …

The detection of anomalous behaviors in automated video surveillance is a recurrent topic in
recent computer vision research. Depending on the application field, anomalies can present …

In emergency situations, actions that save lives and limit the impact of hazards are crucial. In
order to act, situational awareness is needed to decide what to do. Geolocalized photos and …

Crowd counting from unconstrained scene images is a crucial task in many real-world
applications like urban surveillance and management, but it is greatly challenged by the …