Cross-scene crowd counting via deep convolutional neural networks
Cross-scene crowd counting is a challenging task where no laborious data annotation is
required for counting people in new target surveillance crowd scenes unseen in the training …
required for counting people in new target surveillance crowd scenes unseen in the training …
Switching convolutional neural network for crowd counting
D Babu Sam, S Surya… - Proceedings of the …, 2017 - openaccess.thecvf.com
We propose a novel crowd counting model that maps a given crowd scene to its density.
Crowd analysis is compounded by myriad of factors like inter-occlusion between people due …
Crowd analysis is compounded by myriad of factors like inter-occlusion between people due …
Single-image crowd counting via multi-column convolutional neural network
This paper aims to develop a method that can accurately estimate the crowd count from an
individual image with arbitrary crowd density and arbitrary perspective. To this end, we have …
individual image with arbitrary crowd density and arbitrary perspective. To this end, we have …
Crowd counting via scale-adaptive convolutional neural network
L Zhang, M Shi, Q Chen - 2018 IEEE winter conference on …, 2018 - ieeexplore.ieee.org
The task of crowd counting is to automatically estimate the pedestrian number in crowd
images. To cope with the scale and perspective changes that commonly exist in crowd …
images. To cope with the scale and perspective changes that commonly exist in crowd …
End-to-end crowd counting via joint learning local and global count
Crowd counting is a very challenging task in crowded scenes due to heavy occlusions,
appearance variations and perspective distortions. Current crowd counting methods …
appearance variations and perspective distortions. Current crowd counting methods …
Multi-scale convolutional neural networks for crowd counting
Crowd counting on static images is a challenging problem due to scale variations. Recently
deep neural networks have been shown to be effective in this task. However, existing neural …
deep neural networks have been shown to be effective in this task. However, existing neural …
Decidenet: Counting varying density crowds through attention guided detection and density estimation
In real-world crowd counting applications, the crowd densities vary greatly in spatial and
temporal domains. A detection based counting method will estimate crowds accurately in …
temporal domains. A detection based counting method will estimate crowds accurately in …
Second-order convolutional network for crowd counting
L Wang, Q Zhai, B Yin, H Bilal - Fourth International workshop …, 2019 - spiedigitallibrary.org
Single image crowd counting remains challenging primarily due to various issues, such as
large scale variations, perspective and non-uniform crowd distribution. In this paper, we …
large scale variations, perspective and non-uniform crowd distribution. In this paper, we …
Crowd counting with crowd attention convolutional neural network
Crowd counting is a challenging problem due to the scene complexity and scale variation.
Although deep learning has achieved great improvement in crowd counting, scene …
Although deep learning has achieved great improvement in crowd counting, scene …
Exploiting sample correlation for crowd counting with multi-expert network
Crowd counting is a difficult task because of the diversity of scenes. Most of the existing
crowd counting methods adopt complex structures with massive backbones to enhance the …
crowd counting methods adopt complex structures with massive backbones to enhance the …