在弱监督下用于视频异常检测的认知精细增强
Junyeop Lee1, Hyunbon Koo2, Seongjun Kim2
1School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
这项研究引入了使用多个实例学习 (MIL) 和内存单元进行弱监督视频异常检测 (WSVAD) 的新框架. 该方法通过增强特征表示和改善正常和异常视频实例之间的距离指标来减少错误警报.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 弱监督视频异常检测 (WSVAD) 方法使用标记数据评估视频中的异常水平.
- 在WSVAD的一个重大挑战是错误报警的高率,通常是由于模型训练期间框架标签的反射差.
- 多重实例学习 (MIL) 已被探索,通过识别正常和异常部分之间的独特特征来解决这个问题.
研究的目的:
- 为弱监督视频异常检测 (WSVAD) 提出一个新的多实例学习 (MIL) 框架.
- 为了增强功能表示,并弥合正常和异常视频实例之间的差距.
- 减少虚假报警,提高视频中异常检测的准确性.
主要方法:
- 一个新的 MIL 框架,包含一个用于功能增强的内存单元.
- 集成多头注意力功能增强模块.
- 一个损失函数,结合KL分歧和高斯分布估计,以改进距离指标.
主要成果:
- 拟议的框架有效地增强了使用内存的功能,弥合了正常和异常实例之间的差距.
- 该方法成功地识别了可区分的特征,并确保了实例间的距离.
- 在XD-Violence和UCF-Crime数据集上的实验证明了拟议的WSVAD模型的有效性.
结论:
- 该研究为WSVAD提供了一个基于MIL的新型框架,提供了功能增强的高效集成策略.
- 拟议的方法通过改进特征表示和距离指标,有效地减轻了错误警报.
- 该模型在基准数据集上表现出强的表现,验证了其在视频异常检测方面的有效性.
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