NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization

By Qi Wang, Junyu Gao, Wei Lin, Xuelong Li. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020

518 citations recorded by the source corpus.

In the last decade, crowd counting and localization attract much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space design, etc. Many convolutional neural networks (CNN) are designed for tackling this task. However, currently released datasets are so small-scale that they can not meet the needs of the supervised CNN-based algorithms. To remedy this problem, we construct a large-scale congested crowd counting and localization dataset, NWPU-Crowd, consisting of 5,109 images, in a total of 2,133,375 annotated heads with points and boxes. Compared with other real-world datasets, it contains various illumination scenes and has the largest density range (<inline-formula><tex-math notation="LaTeX">$0\!\sim \!20,033$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>0</mml:mn><mml:mspace width="-0.166667em"/><mml:mo>∼</mml:mo><mml:mspace width="-0.166667em"/><mml:mn>20</mml:mn><mml:mo>,</mml:mo><mml:mn>033</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq1-3013269.gif"/></alternatives></inline-formula>). Besides, a benchmark website is developed for impartially evaluating the different methods, which allows researchers to submit the results of the test set. Based on the proposed dataset, we further describe the data characteristics, evaluate the performance of some mainstream state-of-the-art (SOTA) methods, and analyze the new problems that arise on the new data. What’s more, the benchmark is deployed at <uri>https://www.crowdbenchmark.com/</uri>, and the dataset/code/models/results are available at <uri>https://gjy3035.github.io/NWPU-Crowd-Sample-Code/</uri>.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9890ddebd68f348f668da59536ae7b57be35ed39

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Citations in the corpus

Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework (2021, methodology): “Other existing localization aware metrics either ignore the significant density variation across crowds [26, 30] or lack the punishment for duplicate predictions [30, 35].”
Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, background): “ots and camera-based solutions have been proposed by estimating the connections between smartphones and WI-FI access points or Bluetooth beacons [126] or estimating the crowd density of a crowd image [127]. Although counting the detected faces or heads in a crowd image can be used for crowd counting”
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method (2020, background): “Wang et al.[44] introduced a synthetic crowd counting dataset that is based on GTA V electronic game.”
Towards Large-Scale Small Object Detection: Survey and Benchmarks (2022, unknown): “NWPU-Crowd [117] Crowd counting TPAMI 2021 5109 2.”