  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.
