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Cross‐Scale Spatiotemporal Memory‐Augmented Network for Unsupervised Video Anomaly Detection

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The novel framework achieves state-of-the-art performance in video anomaly detection, enhancing detection accuracy.
Utilizes a hierarchical feature pyramid for multi-granularity representation, capturing both local and global patterns.
Introduces a channel-adaptive attention mechanism to model long-range dependencies effectively amidst complex scenarios.
Extensive validation on benchmark datasets UCSD Ped1/2, CUHK Avenue, and ShanghaiTech confirms its robustness against noise.

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Proposed framework enhances video anomaly detection in intelligent surveillance, suggesting improved accuracy and robustness.

Pan et al. (2025) studied this question.

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