Robust Video Anomaly Detection via Hierarchical Motion Decomposition: Extensions of HY-Himmel Architecture
Keywords:
video anomaly detection, hierarchical motion decomposition, HY-Himmel architecture, robustness, socio-technical systems, fairness, sustainability, policy implicationsAbstract
Video anomaly detection remains a critical yet challenging task for large-scale surveillance infrastructures, where the ability to identify rare, unexpected behaviors in crowded or dynamic environments is essential for public safety, operational efficiency, and system governance. Existing deep learning approaches often suffer from limited generalization across domains, high false alarm rates, and poor interpretability, especially when confronted with subtle or multi-scale motion patterns. This paper presents a comprehensive extension of the HY-Himmel architecture, which introduces hierarchical motion decomposition as a core principle for robust video anomaly detection. By organizing temporal features into interleaved multi-stream representations at multiple resolution levels, the proposed framework enables the system to discriminate between normative motions and genuine anomalies with higher fidelity. We analyze the structural trade-offs inherent in such hierarchical designs, including computational cost, latency, and model complexity, and discuss how these trade-offs influence deployment decisions in real-world socio-technical systems. The paper further examines governance and policy implications, focusing on fairness across demographic groups, privacy preservation, and accountability in automated decision-making. Sustainability aspects are addressed through an evaluation of energy consumption and hardware requirements for continuous operation. Cross-domain comparisons with alternative architectures, including spatiotemporal autoencoders, generative adversarial networks, and graph-based models, highlight the advantages of motion decomposition for robustness under distributional shift. This work positions hierarchical motion decomposition not merely as a technical innovation but as a foundational design principle for equitable, interpretable, and sustainable anomaly detection systems. The findings contribute to ongoing discourse on the integration of artificial intelligence into critical infrastructure, emphasizing the need for holistic evaluation criteria that extend beyond accuracy metrics.
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