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2025 , Vol. 42 >Issue 4: 554 - 564

DOI: https://doi.org/10.7523/j.ucas.2023.072

简报

P2P-Loc:点到点微小人物定位

  • 杨溢 ,
  • 余学辉 ,
  • 王岿然 ,
  • 余文文 ,
  • 王子鹏 ,
  • 邹佳凌 ,
  • 韩振军 ,
  • 焦建彬
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  • 中国科学院大学电子电气与通信工程学院, 北京 100049

收稿日期: 2023-02-22

  修回日期: 2023-09-01

  网络出版日期: 2023-09-01

基金资助

中国科学院青年创新促进会(61836012)和国家自然科学基金(61771447)资助

收起

P2P-Loc: point to point tiny person location

  • YANG Yi ,
  • YU Xuehui ,
  • WANG Kuiran ,
  • YU Wenwen ,
  • WANG Zipeng ,
  • ZOU Jialing ,
  • HAN Zhenjun ,
  • JIAO Jianbin
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  • School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2023-02-22

  Revised date: 2023-09-01

  Online published: 2023-09-01

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摘要

边界框是视觉目标定位任务中最常用的标注方法。然而，由于边界框标注对大量精确标注的边界框的依赖，导致其在一些实际场景中难以应用。针对此问题，提出一种新的基于点标注的框架用来定位人体目标，将每个人标注为一个粗略点(CoarsePoint)而不是精确的边界框从而简化标注流程，该点可以是目标范围内的任何点。尽管这极大简化了数据标注的流程和代价，但CoarsePoint标注不可避免地降低了标签的可靠性，并在训练过程中造成网络混乱。因此，提出一种点自优化方法，以自我调整的方式迭代更新点标注。实验结果表明，所提方法有效减轻了标签的不确定性并逐步提高了定位性能，实现目标定位性能的同时可节省高达80%的标注成本。

关键词： 微小人体目标定位; 点监督; 检测精度; 点到点; 边界框标注

本文引用格式

杨溢 , 余学辉 , 王岿然 , 余文文 , 王子鹏 , 邹佳凌 , 韩振军 , 焦建彬 . P2P-Loc:点到点微小人物定位[J]. 中国科学院大学学报, 2025 , 42(4) : 554 -564 . DOI: 10.7523/j.ucas.2023.072

Abstract

Bounding-box annotation form has been the most frequently used method for visual object localization tasks. However, bounding-box annotation relies on a large amount of precisely annotating bounding boxes, and it is expensive and laborious. It is impossible to be employed in practical scenarios and even redundant for some applications (such as tiny person localization) that the size would not matter. Therefore, we propose a novel point-based framework for the person localization task by annotating each person as a coarse point (CoarsePoint) instead of an accurate bounding box that can be any point within the object extent. Then, the network predicts the person’s location as a 2D coordinate in the image. Although this greatly simplifies the data annotation pipeline, the CoarsePoint annotation inevitably decreases label reliability (label uncertainty) and causes network confusion during training. As a result, we propose a point self-refinement approach that iteratively updates point annotations in a self-paced way. The proposed refinement system alleviates the label uncertainty and progressively improves localization performance. Experimental results show that our approach has achieved comparable object localization performance while saving up to 80% of annotation cost.

Key words： tiny person localization; point-based supervision; detection accuracy; point to point; bounding-box annotation

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