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GICEDCam:一个地理空间物联网框架,用于在摄像头流中检测复杂事件

Sepehr Honarparvar¹, Yasaman Honarparvar², Zahra Ashena¹

¹Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.

Sensors (Basel, Switzerland)

|September 13, 2025

概括

GICEDCAM通过在各层分布处理来改进复杂事件检测 (CED),从而降低延迟和成本. 空间事件校正器进一步减少了摄像头流分析中的错误,以提高安全性和监控.

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:

复杂事件检测 (CED) 对于在安全,保安和监控方面分析摄像头流至关重要.
现有的CED框架面临着高资源需求,可扩展性问题和不准确性 (错误阳性/负面).
局限的时空标签和昂贵的培训阻碍了当前CED方法的有效性.

研究的目的:

提出GICEDCAM,一个新的框架,用于高效和可扩展的复杂事件检测.
为了降低CED中的计算成本和端到端延迟.
通过尽量减少假阳性和假阴性来提高空间事件检测的准确性.

主要方法:

GICEDCAM通过边缘,无状态和有状态层分发CED处理.
空间事件校正器组件使用地理空间数据分析来提高准确性.
评估涉及16个摄像头流,分析了四个复杂事件与基线对比.

主要成果:

GICEDCAM实现了端到端延迟减少36%,总计算成本减少45%.
随着每对象数量增加,性能收益增加.
贝叶斯网络 (BN) 提供了最低的延迟,长短期内存 (LSTM) 提供了最高的准确性,轨迹分析提供了最佳的权衡.

结论:

GICEDCAM为复杂事件检测提供了一个可扩展和计算高效的解决方案.
空间事件校正器有效地减少了空间事件检测中的错误.
不同的校正器变体在特定应用程序的准确性和延迟方面提供了明显的优势.

关键词:

云计算是云计算中的一个.复杂事件检测复杂事件检测计算机视觉计算机视觉物联网的东西互联网.对象检测检测对象检测对象检测对象跟踪是指对象的跟踪.空间关系检测空间关系检测运行轨迹分析的方法视频处理视频处理

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