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Due to its variety of applications in the real-world, the task of singleimage-based crowd counting has received a lot of interest in the recent years.Recently, several approaches have been proposed to address various problemsencountered in crowd counting. These approaches are essentially based onconvolutional neural networks that require large amounts of data to train thenetwork parameters. Considering this, we introduce a new large scaleunconstrained crowd counting dataset (JHU-CROWD++) that contains "4,372" imageswith "1.51 million" annotations. In comparison to existing datasets, theproposed dataset is collected under a variety of diverse scenarios andenvironmental conditions. Specifically, the dataset includes several imageswith weather-based degradations and illumination variations, making it a verychallenging dataset. Additionally, the dataset consists of a rich set ofannotations at both image-level and head-level. Several recent methods areevaluated and compared on this dataset. The dataset can be downloaded fromhttp://www.crowd-counting.com .  Furthermore, we propose a novel crowd counting network that progressivelygenerates crowd density maps via residual error estimation. The proposed methoduses VGG16 as the backbone network and employs density map generated by thefinal layer as a coarse prediction to refine and generate finer density maps ina progressive fashion using residual learning. Additionally, the residuallearning is guided by an uncertainty-based confidence weighting mechanism thatpermits the flow of only high-confidence residuals in the refinement path. Theproposed Confidence Guided Deep Residual Counting Network (CG-DRCN) isevaluated on recent complex datasets, and it achieves significant improvementsin errors.

JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method