在人类姿势分类的自组织地图中对集群质量的比较分析
Lisiane Esther Ekemeyong Awong1, Teresa Zielinska1
1Faculty of Power and Aeronautical Engineering, Division of Theory of Machines and Robots, Warsaw University of Technology, 00-665 Warszawa, Poland.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了一种新的方法,用于确定使用无监督学习的最佳人体姿势数量. 新型分辨分数 (DS) 可以在没有手动标记的情况下有效地识别姿势组,提高了人类活动识别的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人类活动识别 人类活动识别
背景情况:
- 无监督的集群算法对于人类姿势识别是有效的.
- 现有的方法往往忽略了选择最佳数量的集群 (姿势).
- 手动数据标签是主观的,耗时的,并且由于数据的复杂性而困难.
研究的目的:
- 开发一种方法来选择合适数量的集群用于人类姿势识别.
- 引入和验证一个新的集群质量评分,分辨分数 (DS).
- 实现使用无监督学习的姿势组的客观选择.
主要方法:
- 利用神经网络与无监督的自我组织地图进行姿势聚类.
- 引入了分辨分数 (DS) 作为一种新的集群质量评估.
- 使用RGB-D摄像头数据对已建立的得分 (轮,Dunn,Calinski-Harabasz,Davies-Bouldin) 进行了DS的评估.
主要成果:
- 区分得分 (DS) 显示了姿势识别的高质量.
- DS有效地跟踪人体姿势的转变和人类活动中的相似之处.
- 对比分析验证了DS在此任务中的优越性,而不是其他质量评分.
结论:
- 拟议的方法提供了一个客观的方法来确定人体姿势的数量.
- 分辨分数 (DS) 是用于无监督人类姿势分类的可靠工具.
- 这项工作通过自动化集群号码选择来推进人类活动识别.
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