基于双流 I3D 卷积网络的监控视频弱监督异常检测
Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network
AI总结：
本文提出基于双流 I3D 卷积网络和多示例学习（MIL）的弱监督异常检测框架，通过排序机制处理视频片段，显著提升监控视频中异常检测的准确性与效率，减少对人工标注的依赖。
摘要：
城市监控系统的广泛部署 necessitated 更复杂的异常检测技术以确保更高的公共安全。本文通过应用双流膨胀 3D（I3D）卷积网络，在异常检测领域取得了重大进展。这些网络通过更有效地从监控视频中提取空间和时间特征，从而提高异常检测的精度，在性能上大幅超越了传统的 3D 卷积网络（C3D）。我们的研究通过实施基于多示例学习（MIL）的弱监督学习框架推进了该领域，该框架独特地将监控视频概念化为包含实例（视频片段）的“包”集合。每个实例通过一个排序机制进行创新性处理，该机制根据片段显示异常的潜力对其进行优先级排序。这种新颖的策略不仅提高了异常检测的准确性和精度，还显著减少了对大量手动标注的依赖。此外，通过对模型设置的细致优化（包括优化器的选择），我们的方法不仅在异常检测系统的性能上确立了新的基准，还为现实世界的监控应用提供了可扩展且高效的解决方案。本文通过提供一个更具适应性、高效且具备上下文感知能力的异常检测系统，对计算机视觉领域做出了重大贡献，该系统有望重新定义城市监控的实践。
英文摘要：
The widespread implementation of urban surveillance systems has necessitated more sophisticated techniques for anomaly detection to ensure enhanced public safety. This paper presents a significant advancement in the field of anomaly detection through the application of Two-Stream Inflated 3D (I3D) Convolutional Networks. These networks substantially outperform traditional 3D Convolutional Networks (C3D) by more effectively extracting spatial and temporal features from surveillance videos, thus improving the precision of anomaly detection. Our research advances the field by implementing a weakly supervised learning framework based on Multiple Instance Learning (MIL), which uniquely conceptualizes surveillance videos as collections of 'bags' that contain instances (video clips). Each instance is innovatively processed through a ranking mechanism that prioritizes clips based on their potential to display anomalies. This novel strategy not only enhances the accuracy and precision of anomaly detection but also significantly diminishes the dependency on extensive manual annotations. Moreover, through meticulous optimization of model settings, including the choice of optimizer, our approach not only establishes new benchmarks in the performance of anomaly detection systems but also offers a scalable and efficient solution for real-world surveillance applications. This paper contributes significantly to the field of computer vision by delivering a more adaptable, efficient, and context-aware anomaly detection system, which is poised to redefine practices in urban surveillance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
