Due to its variety of applications in the real-world, the task of single
image-based crowd counting has received a lot of interest in the recent years.
Recently, several approaches have been proposed to address various problems
encountered in crowd counting. These approaches are essentially based on
convolutional neural networks that require large amounts of data to train the
network parameters. Considering this, we introduce a new large scale
unconstrained crowd counting dataset (JHU-CROWD++) that contains "4,372" images
with "1.51 million" annotations. In comparison to existing datasets, the
proposed dataset is collected under a variety of diverse scenarios and
environmental conditions. Specifically, the dataset includes several images
with weather-based degradations and illumination variations, making it a very
challenging dataset. Additionally, the dataset consists of a rich set of
annotations at both image-level and head-level. Several recent methods are
evaluated and compared on this dataset. The dataset can be downloaded from
this http URL .
Furthermore, we propose a novel crowd counting network that progressively
generates crowd density maps via residual error estimation. The proposed method
uses VGG16 as the backbone network and employs density map generated by the
final layer as a coarse prediction to refine and generate finer density maps in
a progressive fashion using residual learning. Additionally, the residual
learning is guided by an uncertainty-based confidence weighting mechanism that
permits the flow of only high-confidence residuals in the refinement path. The
proposed Confidence Guided Deep Residual Counting Network (CG-DRCN) is
evaluated on recent complex datasets, and it achieves significant improvements
in errors.

介绍了一种用于人群计数的 CNN 网络，并使用采集自多个不同场景的大规模数据集评估了这种方法。该网络使用残差学习以及基于不确定性的置信加权机制进行创新，从而获得了显著的计数准确度改善。