  Pedestrian detection has achieved significant progress with the availability
of existing benchmark datasets. However, there is a gap in the diversity and
density between real world requirements and current pedestrian detection
benchmarks: 1) most of existing datasets are taken from a vehicle driving
through the regular traffic scenario, usually leading to insufficient
diversity; 2) crowd scenarios with highly occluded pedestrians are still under
represented, resulting in low density. To narrow this gap and facilitate future
pedestrian detection research, we introduce a large and diverse dataset named
WiderPerson for dense pedestrian detection in the wild. This dataset involves
five types of annotations in a wide range of scenarios, no longer limited to
the traffic scenario. There are a total of $13,382$ images with $399,786$
annotations, i.e., $29.87$ annotations per image, which means this dataset
contains dense pedestrians with various kinds of occlusions. Hence, pedestrians
in the proposed dataset are extremely challenging due to large variations in
the scenario and occlusion, which is suitable to evaluate pedestrian detectors
in the wild. We introduce an improved Faster R-CNN and the vanilla RetinaNet to
serve as baselines for the new pedestrian detection benchmark. Several
experiments are conducted on previous datasets including Caltech-USA and
CityPersons to analyze the generalization capabilities of the proposed dataset
and we achieve state-of-the-art performances on these previous datasets without
bells and whistles. Finally, we analyze common failure cases and find the
classification ability of pedestrian detector needs to be improved to reduce
false alarm and miss detection rates. The proposed dataset is available at
http://www.cbsr.ia.ac.cn/users/sfzhang/WiderPerson
