增强的视频异常检测通过双三位数对比损失对硬样本歧视
Chunxiang Niu1, Siyu Meng1, Rong Wang1
1College of Information and Cyber Security, People's Public Security University of China, Beijing 100038, China.
Entropy (Basel, Switzerland)
|July 29, 2025
概括
这项研究引入了用于视频异常检测的双三倍对比损失,改善了硬样本识别. 该方法增强了特征歧视,大大减少了异常事件检测中的错误警报.
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:
- 视频异常检测对于安全和监视至关重要.
- 通常使用多个实例学习 (MIL) 框架.
- 现有的MIL方法难以识别硬样本,导致高错误报警率.
研究的目的:
- 为改进视频异常检测提出一种新的双三重对比损失策略.
- 增强特征的辨别能力,以区分异常和正常实例.
- 解决现有方法在识别和区分硬样本方面的局限性.
主要方法:
- 双重三重对比损失策略使用双重内存单元来提取硬,负,正和样本.
- 对比性损失,以限制硬样本和其他样本类型之间的距离.
- 多尺度特征感知和适应性全球-本地特征融合模块,用于增强特征表示.
主要成果:
- 拟议的方法有效地识别硬样本,改善特征歧视.
- 在UCF-Crime数据集上获得了87.16%的AUC得分.
- 在XD-Violence数据集上获得了83.47%的AP分数.
结论:
- 双重三重对比损失策略显著提高了视频异常检测性能.
- 该方法有效地解决了硬样本识别的挑战,减少了错误判断.
- 拟议的方法为现实世界的异常检测应用提供了一个有希望的解决方案.
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