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.

翻译：自深度卷积神经网络的突破以来，人体及其相关部位（如人脸、头部或手部）的检测已得到深入研究并取得显著改善。然而，大多数检测器是独立训练的，这使得将检测到的身体部位与人物进行关联成为一项具有挑战性的任务。本文聚焦于人体及其对应部位的联合检测问题。具体而言，我们提出了一种新颖的扩展对象表示方法，该方法整合了人体或部位的中心位置偏移量，并构建了密集的单阶段基于锚点的身体部位联合检测器（BPJDet）。BPJDet中的身体-部位关联被嵌入到包含语义和几何信息的统一表示中。因此，BPJDet避免了易出错的后处理关联匹配，并实现了更好的准确率与速度权衡。此外，BPJDet可无缝泛化以联合检测任何身体部位。为验证我们方法的有效性与优越性，我们在CityPersons、CrowdHuman和BodyHands数据集上进行了大量实验。所提出的BPJDet检测器在这三个基准上实现了最先进的关联性能，同时保持了高检测精度。代码位于https://github.com/hnuzhy/BPJDet。