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

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....

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2022-05, Vol.44 (5), p.2594-2609
Main Authors: Sindagi, Vishwanath A., Yasarla, Rajeev, Patel, Vishal M.
Format: Article
Language:English
Subjects:
Algorithms
Annotations
Benchmark testing
Benchmarking
Computer networks
Crowd counting
Crowding
dataset
Datasets
Density
Head
Learning
Learning systems
Meteorology
Task analysis
Training
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Summary: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.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2020.3035969