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 …

Crowd counting has recently attracted increasing interest in computer vision but remains a
challenging problem. In this paper, we propose a trellis encoder-decoder network (TEDnet) …

The accuracy of crowd counting in images has improved greatly in recent years due to the
development of deep neural networks for predicting crowd density maps. However, most …

Crowd counting is a fundamental yet challenging task, which desires rich information to
generate pixel-wise crowd density maps. However, most previous methods only used the …

We address the problem of crowd localization, ie, the prediction of dots corresponding to
people in a crowded scene. Due to various challenges, a localization method is prone to …

Crowd counting is an important topic in computer vision due to its practical usage in
surveillance systems. The typical design of crowd counting algorithms is divided into two …

Crowd counting in single-view images has achieved outstanding performance on existing
counting datasets. However, single-view counting is not applicable to large and wide scenes …

Because of the powerful learning capability of deep neural networks, counting performance
via density map estimation has improved significantly during the past several years …

In this paper, we propose a novel SpatioTemporal convolutional Dense Network (STDNet) to
address the video-based crowd counting problem, which contains the decomposition of 3D …

Computer vision tasks often have side information available that is helpful to solve the task.
For example, for crowd counting, the camera perspective (eg, camera angle and height) …