  The detection of human body and its related parts (e.g., face, head or hands)
have been intensively studied and greatly improved since the breakthrough of
deep CNNs. However, most of these detectors are trained independently, making
it a challenging task to associate detected body parts with people. This paper
focuses on the problem of joint detection of human body and its corresponding
parts. Specifically, we propose a novel extended object representation that
integrates the center location offsets of body or its parts, and construct a
dense single-stage anchor-based Body-Part Joint Detector (BPJDet). Body-part
associations in BPJDet are embedded into the unified representation which
contains both the semantic and geometric information. Therefore, BPJDet does
not suffer from error-prone association post-matching, and has a better
accuracy-speed trade-off. Furthermore, BPJDet can be seamlessly generalized to
jointly detect any body part. To verify the effectiveness and superiority of
our method, we conduct extensive experiments on the CityPersons, CrowdHuman and
BodyHands datasets. The proposed BPJDet detector achieves state-of-the-art
association performance on these three benchmarks while maintains high accuracy
of detection. Code is in https://github.com/hnuzhy/BPJDet.
