IEEE Trans Pattern Anal Mach Intell. 2022 May;44(5):2594-2609. doi: 10.1109/TPAMI.2020.3035969. Epub 2022 Apr 1.
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 http://www.crowd-counting.com. 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.
我们引入了一个新的大规模无约束人群计数数据集（JHU-CROWD++），其中包含“4372”张图像和“151 万”个注释。与现有数据集相比，该数据集是在各种不同的场景和环境条件下收集的。具体来说，该数据集包括一些具有天气退化和光照变化的图像，使其成为一个非常具有挑战性的数据集。此外，该数据集还包含丰富的图像级和头部级注释。我们在该数据集上评估和比较了几种最新的方法。该数据集可从 http://www.crowd-counting.com 下载。此外，我们提出了一种新的人群计数网络，通过残差误差估计逐步生成人群密度图。该方法使用 VGG16 作为骨干网络，并使用最后一层生成的密度图作为粗预测，通过残差学习以渐进的方式细化和生成更精细的密度图。此外，残差学习受到基于不确定性的置信度加权机制的指导，该机制允许仅在细化路径中流动高置信度的残差。我们在最近的复杂数据集上评估了所提出的置信度引导深度残差计数网络（CG-DRCN），并在误差方面取得了显著的改进。
