  Vision-based person, hand or face detection approaches have achieved
incredible success in recent years with the development of deep convolutional
neural network (CNN). In this paper, we take the inherent correlation between
the body and body parts into account and propose a new framework to boost up
the detection performance of the multi-level objects. In particular, we adopt a
region-based object detection structure with two carefully designed detectors
to separately pay attention to the human body and body parts in a
coarse-to-fine manner, which we call Detector-in-Detector network (DID-Net).
The first detector is designed to detect human body, hand, and face. The second
detector, based on the body detection results of the first detector, mainly
focus on the detection of small hand and face inside each body. The framework
is trained in an end-to-end way by optimizing a multi-task loss. Due to the
lack of human body, face and hand detection dataset, we have collected and
labeled a new large dataset named Human-Parts with 14,962 images and 106,879
annotations. Experiments show that our method can achieve excellent performance
on Human-Parts.
