In machine learning, two approaches outperform traditional algorithms: ensemble learning
and deep learning. The former refers to methods that integrate multiple base models in the …

Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

摘要随着摄像头安装数量的日益增多, 以及智慧城市和公共安全需求的日益增长,
采用人工的视频监控方式已经远远不能满足需要, 因此智能视频监控技术应运而生并迅速成为 …

Localizing individuals in crowds is more in accordance with the practical demands of
subsequent high-level crowd analysis tasks than simply counting. However, existing …

Previous work generally believes that improving the spatial invariance of convolutional
networks is the key to object counting. However, after verifying several mainstream counting …

In crowd counting, each training image contains multiple people, where each person is
annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth …

Previous work shows that a better density map representation can improve the performance
of crowd counting. In this paper, we investigate learning the density map representation …

In crowd counting datasets, each person is annotated by a point, which is usually the center
of the head. And the task is to estimate the total count in a crowd scene. Most of the state-of …

In the last decade, crowd counting and localization attract much attention of researchers due
to its wide-spread applications, including crowd monitoring, public safety, space design, etc …

Recently, counting the number of people for crowd scenes is a hot topic because of its
widespread applications (eg video surveillance, public security). It is a difficult task in the …