Semantic Embedding Learning-Based Video Anomalous Behavior Detection

Video anomalous behavior detection remains a key research focus in computer vision and digital forensics. The explosive growth of short-video platforms has flooded networks with violent, illegal, and inappropriate content, posing significant challenges to platform regulation and cybersecurity. Automated video analysis through computer vision algorithms enables realtime content moderation for platforms while generating critical digital evidence for judicial investigations. But most existing methods are incapable of performing supervised learning on fewshot anomaly datasets or generalizing to more complex video scenarios. We propose a contrastive semantic embedding framework to address few-shot anomaly detection challenges. Our method jointly encodes video sequences and textual anomaly descriptors into a unified embedding space through pre-training on largescale action datasets, followed by few-shot fine-tuning. The learned space enables anomaly prediction via cross-modal similarity measurement. Additionally, we introduce StreamAnomaly, a realistic benchmark containing diverse anomalous behaviors. Experiments demonstrate that compared to other methods, our model exhibits outstanding performance and high accuracy in recognizing anomalies in both traditional datasets and the new benchmark datasets.