  Pedestrian detection in crowded scenes is a challenging problem, because
occlusion happens frequently among different pedestrians. In this paper, we
propose an effective and efficient detection network to hunt pedestrians in
crowd scenes. The proposed method, namely PedHunter, introduces strong
occlusion handling ability to existing region-based detection networks without
bringing extra computations in the inference stage. Specifically, we design a
mask-guided module to leverage the head information to enhance the feature
representation learning of the backbone network. Moreover, we develop a strict
classification criterion by improving the quality of positive samples during
training to eliminate common false positives of pedestrian detection in crowded
scenes. Besides, we present an occlusion-simulated data augmentation to enrich
the pattern and quantity of occlusion samples to improve the occlusion
robustness. As a consequent, we achieve state-of-the-art results on three
pedestrian detection datasets including CityPersons, Caltech-USA and
CrowdHuman. To facilitate further studies on the occluded pedestrian detection
in surveillance scenes, we release a new pedestrian dataset, called SUR-PED,
with a total of over 162k high-quality manually labeled instances in 10k
images. The proposed dataset, source codes and trained models will be released.
