BPJDet:用于通用身体部位联合检测的扩展对象表示
IEEE transactions on pattern analysis and machine intelligence
|January 16, 2024
概括
这项研究引入了一种新的身体部位联合探测器 (BPJDet),用于统一的人体和部分检测. BPJDet提高了关联准确性,并对各种数据集和动物进行了概括,增强了下游应用.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 现有的基于卷积神经网络 (CNN) 的人体和部分检测方法通常独立训练,阻碍了准确的部分与身体的关联.
- 挑战在于有效地将检测到的身体部位与复杂场景中的整体人体形式联系起来.
研究的目的:
- 制定一个统一的框架,共同检测人体及其组成部分.
- 引入一种新的对象表示方式,将身体部分整合起来,以改善关联.
- 为了创建一个端到端的通用探测器,身体部位联合探测器 (BPJDet),能够协同优化多任务.
主要方法:
- 提出了一种新的扩展对象表示,包括身体部位的中心偏移.
- 构建了一个端到端的通用身体部位联合探测器 (BPJDet).
- 使用了嵌入体部关联与语义和几何信息的统一表示,适用于基于和无探测器.
主要成果:
- BPJDet在多个身体部位和身体部位数据集 (CityPersons,CrowdHuman,BodyHands,COCOHumanParts,Animals5C) 中展示了最先进的关联性能.
- 实现了高检测准确度,同时保持了有利的速度-准确性权衡,避免了易发生错误的后匹配.
- 展示了用于检测人类和四足动物的部分的概括能力.
结论:
- 拟议的BPJDet通过综合代表性有效地解决了联合人体和部分检测的挑战.
- BPJDet提供卓越的身体部位关联精度和多功能性,优于现有方法.
- 增强的关联能力显著提高了下游应用程序的性能,如人群头部检测和手接触估计.
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