NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization
Northwestern Polytechnical University
Abstract
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…
Citation impact
- FWCI
- 26.97
- Percentile
- 100%
- References
- 69
Authors
4Topics & keywords
- Benchmark (surveying)
- Computer science
- Convolutional neural network
- Code (set theory)
- Scale (ratio)
- Task (project management)
- Set (abstract data type)
- Artificial intelligence