  Human detection has witnessed impressive progress in recent years. However,
the occlusion issue of detecting human in highly crowded environments is far
from solved. To make matters worse, crowd scenarios are still under-represented
in current human detection benchmarks. In this paper, we introduce a new
dataset, called CrowdHuman, to better evaluate detectors in crowd scenarios.
The CrowdHuman dataset is large, rich-annotated and contains high diversity.
There are a total of $470K$ human instances from the train and validation
subsets, and $~22.6$ persons per image, with various kinds of occlusions in the
dataset. Each human instance is annotated with a head bounding-box, human
visible-region bounding-box and human full-body bounding-box. Baseline
performance of state-of-the-art detection frameworks on CrowdHuman is
presented. The cross-dataset generalization results of CrowdHuman dataset
demonstrate state-of-the-art performance on previous dataset including
Caltech-USA, CityPersons, and Brainwash without bells and whistles. We hope our
dataset will serve as a solid baseline and help promote future research in
human detection tasks.
