时空不确定性指导的非最大抑制用于视频事件检测
Fengqian Pang1, Chunyue Lei2, Yunjian He2
1School of Information Science and Technology, North China University of Technology, Beijing, 100144, China. fqpang@ncut.edu.cn.
Scientific reports
|April 8, 2025
概括
这项研究引入了一种用于视频事件检测 (VED) 的新型神经网络,该网络可以估计空间和时间的不确定性. 这种不确定性引导非最大抑制 (NMS),提高了计算机视觉应用中的检测精度.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 视频事件检测 (VED) 对于自动驾驶和监控等应用至关重要.
- 现有的VED方法主要关注网络架构,忽视不确定性估计.
- 不确定性估计对于在VED系统中可靠的决策至关重要.
研究的目的:
- 提出一个端到端的VED神经网络,结合空间和时间的不确定性.
- 开发一种由估计不确定性指导的新型非最大抑制 (NMS) 方法,称为空间时间不确定性指导的NMS (STU-NMS).
主要方法:
- 为VED开发了一个端到端的神经网络.
- 集成空间和时间不确定性估计到VED模型中.
- 拟议的STU-NMS,它利用估计的不确定性来改进检测输出.
主要成果:
- 采用空间时间不确定性 (STU) 集成的拟议方法优于不模拟不确定性的现有技术.
- 在基准数据集 (J-HMDB-21,UCF101-24,AVA) 上,STU-NMS显示了更好的检测性能.
结论:
- 整合空间和时间的不确定性显著提高了视频事件检测.
- 拟议的STU-NMS方法通过利用不确定性来提高准确性,为VED提供了一种优越的方法.
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