Multi-view crowd counting has been previously proposed to utilize multi-cameras to extend
the field-of-view of a single camera, capturing more people in the scene, and improve …

While the performance of crowd counting via deep learning has been improved dramatically
in the recent years, it remains an ingrained problem due to cluttered backgrounds and …

In this work, we propose a novel crowd counting network that progressively generates crowd
density maps via residual error estimation. The proposed method uses VGG16 as the …

This paper proposes to handle the practical problem of learning a universal model for crowd
counting across scenes and datasets. We dissect that the crux of this problem is the …

In crowd counting, due to the problem of laborious labelling, it is perceived intractability of
collecting a new large-scale dataset which has plentiful images with large diversity in …

Crowd counting has attracted increasing attentions in recent years due to its challenges and
wide societal applications. Despite persevering efforts made by the research community …

Recent works on crowd counting mainly leverage Convolutional Neural Networks (CNNs) to
count by regressing density maps, and have achieved great progress. In the density map …

To fully leverage the data captured from different scenes with different view angles while
reducing the annotation cost, this paper studies a novel crowd counting setting, ie only using …

Research in neural networks in the field of computer vision has achieved remarkable
accuracy for point estimation. However, the uncertainty in the estimation is rarely addressed …

Most existing crowd counting methods require object location-level annotation which is labor-
intensive and time-consuming to obtain. In contrast, weaker annotations that only label the …