一个基于GAN的统一框架,用于使用光流和RGB线索进行无监督视频异常检测
Seung-Hun Kang1, Hyun-Soo Kang1
1Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
本研究引入了用于视频异常检测的无监督框架,使用新的GAN架构集成外观和运动数据. 该方法在多个数据集上实现了最先进的结果,而不需要标记异常数据.
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:
- 视频异常检测在不受约束的环境中具有挑战性,原因是有限的标记数据和各种场景.
- 现有的方法难以应对现实世界视频数据的复杂性和可变性.
研究的目的:
- 开发一种用于视频异常检测的新型无监督框架.
- 为了有效地整合RGB外观和光学流动运动线索.
- 提高异常检测模型的训练稳定性和重建质量.
主要方法:
- 一个统一的基于GAN (生成对抗网络) 的架构,结合双编码器和GRU注意力时间瓶.
- 使用ConvLSTM层和残余增强MLP进行时间连贯性评估的区分器.
- 介绍DASLoss,一个复合损失函数,包含像素,感知,时间和特征一致性术语.
主要成果:
- 在XD-Violence数据集上实现了80.5%的平均精度 (AP),优于MGAFlow和Flashback等无监督方法.
- 在检测短期暴力事件的曲棍球战数据集上获得了0.92的AUC和0.85的F1得分.
- 在UCSD Ped2数据集上获得了0.96的AUC,与无监督的最先进性能相匹配.
结论:
- 拟议的无监督框架在各种异常检测任务中展示了有效性和通用性.
- 将外观和运动与新的GAN架构和损失函数集成,显著提高了异常检测性能.
- 该方法为在不受约束的环境中检测视频异常提供了强大的解决方案,即使有稀缺的标记数据.
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