In the last decade, crowd counting attracts 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 dataset, NWPU-Crowd, consisting of 5, 109 images, in a total of 2, 133, 238 annotated heads. 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, NWPU-Crowd Dataset is available at http://www.crowdbenchmark.com/, and the code is open-sourced at https://github.com/gjy3035/NWPU-Crowd-Sample-Code.
Most recently, Wang et al. REF released a large-scale crowd counting dataset (NWPUCrowd) consisting of 5,109 images with 2.13 million annotations.
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting
ArXiv
ArXiv
Computer Science