In the context of escalating safety concerns across various domains, the
tasks of Video Anomaly Detection (VAD) and Video Anomaly Recognition (VAR) have
emerged as critically important for applications in intelligent surveillance,
evidence investigation, violence alerting, etc. These tasks, aimed at
identifying and classifying deviations from normal behavior in video data, face
significant challenges due to the rarity of anomalies which leads to extremely
imbalanced data and the impracticality of extensive frame-level data annotation
for supervised learning. This paper introduces a novel hierarchical graph
neural network (GNN) based model MissionGNN that addresses these challenges by
leveraging a state-of-the-art large language model and a comprehensive
knowledge graph for efficient weakly supervised learning in VAR. Our approach
circumvents the limitations of previous methods by avoiding heavy gradient
computations on large multimodal models and enabling fully frame-level training
without fixed video segmentation. Utilizing automated, mission-specific
knowledge graph generation, our model provides a practical and efficient
solution for real-time video analysis without the constraints of previous
segmentation-based or multimodal approaches. Experimental validation on
benchmark datasets demonstrates our model's performance in VAD and VAR,
highlighting its potential to redefine the landscape of anomaly detection and
recognition in video surveillance systems.

这篇论文介绍了一种基于层次图神经网络（GNN）的新型模型 MissionGNN，通过利用先进的大型语言模型和全面的知识图谱，在 VAR 的弱监督学习中解决了异常检测和识别中的挑战。通过自动化的任务特定知识图生成，我们的模型提供了一种实际高效的解决方案，可以在实时视频分析中实现完全的帧级训练，避开了先前基于分割或多模态方法的限制。在基准数据集上的实验验证证明了我们模型在 VAD 和 VAR 中的性能，突显了它在视频监控系统中重新定义异常检测和识别领域的潜力。