IEEE Trans Pattern Anal Mach Intell. 2021 Jun;43(6):2141-2149. doi: 10.1109/TPAMI.2020.3013269. Epub 2021 May 11.
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 ( 0 ∼ 20,033). 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 https://www.crowdbenchmark.com/, and the dataset/code/models/results are available at https://gjy3035.github.io/NWPU-Crowd-Sample-Code/.
在过去的十年中，人群计数和定位由于其广泛的应用而引起了研究人员的极大关注，包括人群监控、公共安全、空间设计等。许多卷积神经网络（CNN）被设计用于解决这个任务。然而，目前发布的数据集规模太小，无法满足基于监督 CNN 的算法的需求。为了解决这个问题，我们构建了一个大规模的拥挤人群计数和定位数据集 NWPU-Crowd，包含 5109 张图像，总共 2133375 个标注的人头点和框。与其他真实世界的数据集相比，它包含了各种光照场景，具有最大的密度范围（0 到 20033）。此外，还开发了一个基准网站，用于公正地评估不同的方法，允许研究人员提交测试集的结果。基于提出的数据集，我们进一步描述了数据特征，评估了一些主流的最先进方法的性能，并分析了新数据带来的新问题。此外，基准测试部署在 https://www.crowdbenchmark.com/，数据集/代码/模型/结果可在 https://gjy3035.github.io/NWPU-Crowd-Sample-Code/ 获得。
