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适应性视频总结的行为分析:从概括到个性化

Payal Kadam^1,2, Deepali Vora¹, Shruti Patil¹

¹Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University) (SIU), Lavale, Pune, Maharashtra, India.

|July 15, 2024

概括

本研究介绍了一种两阶段方法,使用机器学习和用户参与来总结视频. 它通过通过个性化内容策划来提高可访问性和导航性来解决管理CCTV录像的挑战.

科学领域:

计算机科学计算机科学
人工智能的人工智能
多媒体系统多媒体系统

背景情况:

管理大量的多媒体内容,特别是CCTV录像,在存储,可访问性和有效导航方面提出了重大挑战.
现有的视频总结技术往往缺乏个性化,并与数字视频数据的庞大规模作斗争.

研究的目的:

开发一种综合的视频总结技术,将机器学习与用户参与融合在一起.
解决存储,可访问性和导航在管理多媒体内容,特别是CCTV录像中的挑战.
为了增强用户交互并个性化视频摘要的内容策划.

主要方法:

第一个阶段:基于关键检测和行为分析的视频总结,使用YOLOv5进行对象识别,使用Deep SORT进行对象跟踪和使用单击检测器 (SSD).
第二阶段:基于用户兴趣的视频总结,由机器学习驱动,结合用户偏好进行个性化内容策划.
利用自然语言工具包 (NLTK),OpenCV,TensorFlow和EfficientDET模型进行定制的视频摘要生成.

主要成果:

通过客观分析和以用户为中心的个性化改进视频总结的双阶段方法.
增强在数字平台上处理大量视频数据的能力.
根据个人用户偏好的定制视频摘要的生成.

结论:

提出的创新方法有效地解决了管理多媒体数据的复杂挑战.
将机器学习技术与用户参与相结合,在视频总结方面取得了重大进展.
该系统增强了用户交互,并为数字内容管理提供了高效的解决方案.

关键词:

深度学习,计算机视觉深度排序算法深度排序算法信息检索信息检索关键框提取基于单视图查询视频总结视频总结基于查询的总结基于查询的总结视频总结视频总结在YOLO V5中,你会看到YOLO V5.

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