TLDR: A new framework, GV-VAD, uses text-conditioned AI video generation to create synthetic anomaly videos, augmenting scarce real-world data for video anomaly detection. It employs a “synthetic sample loss scaling” strategy to balance real and synthetic data influence during training, leading to improved performance on datasets like UCF-Crime by making models more robust and accurate.
The field of video anomaly detection (VAD) is crucial for public safety, especially in intelligent surveillance systems. However, a major hurdle in developing effective VAD models is the scarcity and high cost of annotating real-world anomalies. Anomalies are rare and unpredictable, making it difficult to gather enough diverse training data. This limitation affects the performance and generalization ability of current VAD models.
To tackle this challenge, researchers have introduced a new framework called Generative Video-Enhanced Weakly-Supervised Video Anomaly Detection, or GV-VAD. This innovative approach uses advanced text-conditioned video generation models to create synthetic videos that are both semantically controllable and physically realistic. These virtual videos serve as a low-cost way to significantly expand the training data.
A key aspect of GV-VAD is its ability to generate diverse synthetic anomaly videos based on specific descriptions. The framework identifies four core elements for defining anomalies: camera viewpoint, location, subject, and the anomalous event itself. These elements are fed into a large language model, like GPT-4o, to produce detailed descriptions for both abnormal and normal events. For example, a description might be generated for a “passenger collapsing at a train station” or “commuters waiting calmly on a platform.” These descriptions then guide a diffusion model, such as CogVideoX, to create the actual synthetic videos.
One of the main concerns with using synthetic data is the “domain gap” – the difference between generated videos and real-world footage. To address this, GV-VAD incorporates a “synthetic sample loss scaling” (SSLS) strategy. This strategy intelligently adjusts the influence of synthetic samples during the training process. By applying a scaling factor, the model can learn from the diverse patterns and scenes in virtual data without becoming overly reliant on or overfitting to the synthetic domain. This ensures that the model remains robust when applied to real videos.
The GV-VAD framework is designed to be compatible with most existing VAD models. In their experiments, the researchers adopted the LAP method for training the anomaly detector. They combined visual features from both synthetic and real videos to create a hybrid training dataset, enhancing the robustness of the video anomaly detector.
Experiments conducted on the UCF-Crime dataset, a large-scale video anomaly detection dataset, demonstrated the effectiveness of GV-VAD. The framework significantly outperformed state-of-the-art methods in terms of frame-level AUC performance. For instance, when integrated with the LAP method, GV-VAD achieved an AUC of 89.3%, surpassing LAP’s baseline of 88.9%. The study also showed that adding synthetic videos consistently improves performance, especially in scenarios with limited real anomaly samples. Even with only 25% of the real data, adding generated videos boosted performance to a level higher than using 50% of the real data alone.
Qualitative analysis further highlighted GV-VAD’s advantages. Compared to baseline methods, GV-VAD provided more accurate and temporally consistent anomaly predictions, showing improved robustness even in complex scenes or those with visual noise, such as poor lighting conditions. This means fewer false alarms and better discrimination between normal and anomalous events.
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In conclusion, GV-VAD offers a promising solution to the challenges of data scarcity in video anomaly detection. By leveraging text-conditioned video generation and an intelligent loss scaling strategy, it provides a cost-effective way to augment training data, leading to more robust and accurate anomaly detection systems for public safety applications. You can find more details about this research in the full paper.
