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 …

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 …

Learning and capturing both appearance and dynamic representations are pivotal for crowd
video understanding. Convolutional Neural Networks (CNNs) have shown its remarkable …

We propose an attention-injective deformable convolutional network called ADCrowdNet for
crowd understanding that can address the accuracy degradation problem of highly …

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 …

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 …

Crowd analysis via computer vision techniques is an important topic in the field of video
surveillance, which has wide-spread applications including crowd monitoring, public safety …

Understanding crowd behavior is a pivotal step toward urban scene analysis. This is
considered a very challenging task and has rarely been addressed to date due to the …

Automated counting of people in crowd images is a challenging task. The major difficulty
stems from the large diversity in the way people appear in crowds. In fact, features available …

We present a novel method called Contextual Pyramid CNN (CP-CNN) for generating high-
quality crowd density and count estimation by explicitly incorporating global and local …