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通过子组分析实时检测和定位暴力

Emmeke Veltmeijer¹, Morris Franken², Charlotte Gerritsen¹

¹Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam, the Netherlands.

Multimedia tools and applications

|February 17, 2025

概括

本研究引入了一种通过跟踪子组,提高社会意识和监控录像中暴力事件的局部化来早期检测暴力 (VD) 的新方法. 该方法增强了现有的实时干预模型.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
监控系统监控系统

背景情况:

现有的暴力检测 (VD) 方法与现实世界监控数据扎,缺乏本地化和社会背景.
及时的人类干预至关重要,但目前的VD系统往往不足以应对复杂的场景.

研究的目的:

为VD开发一个可适应的附加模块,集成子组识别和跟踪.
增强实时VD系统,提高暴力事件的社会意识和本地化.

主要方法:

提出了一种新的方法,将子组跟踪集成到现有的VD模型中.
开发了一个系统来识别和跟踪跨视频的多个子组.
集成的本地化能力,以识别参与暴力事件的团体.

主要成果:

该方法通过定位参与暴力行为的个人来提高实时视频疾病的社会意识.
在SCFD数据集上达到91.3%的准确性,在RWF-2000数据集上达到87.2%.
证明了对未见的数据集的实际实用性和概括性,性能接近最先进的状态.

结论:

拟议的子组集成增强VD系统,提供关键的本地化和社会维度信息.
这种可适应的模块为监视中的早期暴力检测提供了有希望的进展.
该方法的效率和概括能力凸显了它在现实世界中实际应用的可能性.

关键词:

定位局部化定位局部化亚组分析亚组分析监控数据监控数据监控数据发现暴力行为,发现暴力.

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