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提高知觉一致性和校准不确定性,用于实时检测乒乓球球
Chang Hong1, Xinjie Wang2, Shu Zhou3
1College of Physical Education, Guangzhou Sport University, Guangzhou, 510000, China. 11346@gzsport.edu.cn.
使用新的感知一致性增强模块 (PCEM) 和不确定性校准模块 (UCM) 改进了自动桌球检测. 这些方法在复杂的现实场景中提高了准确性和稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自动化系统擅长捕捉乒乓球动态.
- 检测乒乓球面临挑战:小尺寸,快速运动,复杂的轨迹,背景干扰和照明变化.
- 传统的欧几里德嵌入限制了层次结构和目标关系表示.
研究的目的:
- 为了提高乒乓球球探测的准确性和稳定性.
- 解决复杂视觉环境中当前检测方法的局限性.
- 在动态环境中改进特征提取和不确定性量化.
主要方法:
- 拟议的感知一致性增强模块 (PCEM) 用于强大的特征提取.
- 引入了不确定性校准模块 (UCM),用于将特征转换为超表面空间并量化不确定性.
- 利用超表面距离来定量动态不确定性,并针对高不确定性地区进行专注培训.
主要成果:
- 在OpenTTGames和乒乓球数据集上的检测准确度得到了显著改进.
- 展示了增强的跟踪稳定性,特别是在复杂的场景中.
- 与最先进的方法相比,实现了更高的性能.
结论:
- 拟议的PCEM和UCM有效地解决了乒乓球球探测方面的挑战.
- 这些方法在动态和视觉复杂的环境中显著提高了准确性和稳定性.
- 这项工作推动了桌球等动态体育运动的自动化分析.
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