Detection of human body and its parts (e.g., head or hands) has been intensively studied. However, most of these CNNs-based detectors are trained independently, making it difficult to associate detected parts with body. In this paper, we focus on the joint detection of human body and its corresponding parts. Specifically, we propose a novel extended object representation integrating center-offsets of body parts, and construct a dense one-stage generic Body-Part Joint Detector (BPJDet). In this way, body-part associations are neatly embedded in a unified object representation containing both semantic and geometric contents. Therefore, we can perform multi-loss optimizations to tackle multi-tasks synergistically. BPJDet does not suffer from error-prone post matching, and keeps a better trade-off between speed and accuracy. Furthermore, BPJDet can be generalized to detect any one or more body parts. To verify the superiority of BPJDet, we conduct experiments on three body-part datasets (CityPersons, CrowdHuman and BodyHands) and one body-parts dataset COCOHumanParts. While keeping high detection accuracy, BPJDet achieves state-of-the-art association performance on all datasets comparing with its counterparts. Besides, we show benefits of advanced body-part association capability by improving performance of two representative downstream applications: accurate crowd head detection and hand contact estimation. Code is released in https://github.com/hnuzhy/BPJDet.

翻译：人体及其部位（如头部或手部）的检测已得到广泛研究。然而，大多数基于CNN的检测器是独立训练的，难以将检测到的部位与人体相关联。本文聚焦于人体及其对应部位的联合检测问题。具体而言，我们提出一种新颖的扩展对象表示方法，该方法整合了人体部位的中心偏移量，并构建了密集的单阶段通用人体-部位联合检测器（BPJDet）。通过这种方式，人体-部位的关联被巧妙地嵌入到包含语义和几何内容的统一对象表示中。因此，我们可以执行多损失优化以协同处理多任务。BPJDet避免了易出错的后期匹配过程，并在速度与精度之间保持了更优的权衡。此外，BPJDet可泛化至任意一个或多个身体部位的检测。为验证BPJDet的优越性，我们在三个人体部位数据集（CityPersons、CrowdHuman和BodyHands）及一个人体部位数据集COCOHumanParts上进行了实验。在保持高检测精度的同时，BPJDet在所有数据集上与同类方法相比均实现了最先进的关联性能。此外，我们通过提升两个代表性下游应用（精准人群头部检测与手部接触估计）的性能，展示了先进人体-部位关联能力的优势。代码已开源至https://github.com/hnuzhy/BPJDet。