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:自远端CNN突破以来,对人体及其相关部分(如脸部、头部或手部)的探测进行了深入研究,并大大改进了自深层CNN突破以来,对人体及其相关部分(如脸部、头部或手部)的探测进行了深入的研究和极大改进;然而,大多数这些探测器都是独立培训的,因此将发现的身体部分与人联系起来是一项艰巨的任务;本文件侧重于共同探测人体及其相应部分的问题;具体地说,我们建议采用新的扩展对象说明,将身体或部分的中心位置的抵消与身体或其部分相融合,并建立一个密集的单级锚定点人体-部分联合探测器(BBPJDet);BPJDet的人体部分协会已嵌入包含语义和几何学信息的统一代表中。因此,BPJDet并不因易出错的关联后相匹配而受到影响,而是具有更好的精确度-速度交替问题。此外,BPDDT可以无缝地对任何身体部分进行联合检测。为了验证我们的方法的有效性和优越性,我们在CityPersons、CrowdHuman和bodyHandHands数据集上进行广泛的实验。 拟议的BDDDDDDDebs amt ambs ambs ambs的高级检测/debs bestaldaldalds bestmabs balds balds balds balds balds balds balds balds basts/dsmats/daldaldaldaldald.</s>