Audio-Visual Anomaly Detection in Long Surveillance Videos Using Context-Aware Temporal Modeling
Keywords:
audio-visual anomaly detection, surveillance video, temporal modeling, context awareness, multimodal fusion, large-scale systems, socio-technical infrastructureAbstract
The proliferation of video surveillance systems has created an urgent need for automated anomaly detection methods capable of processing long-duration footage with high accuracy and low latency. While existing approaches have explored either visual or audio modalities independently, the fusion of audio-visual signals remains underexplored, particularly in the context of temporally extended video sequences where contextual dependencies are critical. This paper presents a comprehensive framework for audio-visual anomaly detection in long surveillance videos using context-aware temporal modeling. The proposed system architecture integrates a dual-stream encoder for synchronized audio and visual feature extraction, a hierarchical temporal memory module that captures both short-term and long-range dependencies, and a cross-modal attention mechanism that dynamically weights the contribution of each modality based on scene context. We discuss the structural trade-offs inherent in designing such a system, including the balance between temporal resolution and computational cost, the governance of data privacy during model training, and the infrastructure requirements for real-time deployment in edge and cloud environments. The paper further examines sustainability considerations, such as energy consumption during inference on large-scale camera networks, and fairness implications arising from biased training data distributions across different environmental conditions. Through a detailed analysis of deployment scenarios in smart city, industrial, and public transit contexts, we illustrate how context-aware temporal modeling can improve detection robustness while reducing false alarm rates. Finally, we outline policy recommendations for responsible deployment, including transparency in model decision-making and equitable performance across diverse demographic and geographic settings. This work contributes a systems-level perspective that bridges algorithmic innovation with socio-technical governance, offering a roadmap for future research in multimodal surveillance analytics.
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