视频监控中的基于深度学习的异常检测:一项调查
Huu-Thanh Duong1, Viet-Tuan Le1, Vinh Truong Hoang1
1Faculty of Information Technology, Ho Chi Minh City Open University, 97 Vo Van Tan, District 3, Ho Chi Minh City 700000, Vietnam.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本综述详细介绍了用于视频异常检测的深度学习方法,重点关注生成模型. 它对技术进行了分类,并讨论了加强公共安全系统的挑战.
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:
- 视频异常检测对公共安全至关重要,推动对智能监控系统的需求.
- 深度学习,特别是生成模型,已成为该领域的主要技术.
- 现有的调查涵盖了各种异常检测领域,但需要对视频中的深度学习进行全面审查.
研究的目的:
- 提供基于深度学习的视频异常检测技术的全面审查.
- 通过目标和学习指标对深度学习方法进行分类.
- 讨论预处理,特征工程,基准数据集和未来研究方向.
主要方法:
- 基于深度学习的视频异常检测方法的分类.
- 综述生成模型及其在异常检测中的应用.
- 讨论基于视觉的异常检测的预处理和特征工程.
主要成果:
- 深度学习,特别是生成模型,为视频异常检测提供了有效的解决方案.
- 存在各种深度学习方法,根据它们的具体目标和学习指标进行分类.
- 确定了基准数据集和视频监控异常检测中的常见挑战.
结论:
- 深度学习技术对于推进智能视频监控系统至关重要.
- 需要进一步的研究来应对挑战,并提高异常检测模型的稳定性.
- 本综述是视频异常检测领域的研究人员和从业人员的指南.
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