Generative Trajectory Forecasting and Anomaly Detection in Smart Surveillance Videos via Interleaved Multi-Scale Motion Modeling
Keywords:
generative trajectory forecasting, anomaly detection, multi-scale motion modeling, smart surveillance, video understanding, system architecture, fairness, governanceAbstract
The rapid proliferation of video surveillance infrastructure in public and private spaces has created an urgent need for automated systems capable of understanding complex motion patterns and detecting anomalous events with high reliability. Traditional approaches to anomaly detection in surveillance video rely on handcrafted features or shallow learning models that fail to capture the hierarchical nature of human movement across multiple spatial and temporal scales. This paper proposes a comprehensive system framework for generative trajectory forecasting and anomaly detection that leverages interleaved multi-scale motion modeling. The architecture integrates generative trajectory predictors with a multi-stream encoder that processes motion cues at granularities ranging from instantaneous optical flow to long-term behavioral sequences. By interleaving representations across scales, the system achieves a fine-grained understanding of normative motion contexts while maintaining computational tractability for real-time deployment. We discuss structural trade-offs inherent in scaling such models, including the tension between predictive accuracy and inference latency, the governance challenges of deploying generative models in high-stakes environments, and the sustainability implications of large-scale video processing. Cross-domain comparisons with alternative architectures such as spatiotemporal graph networks and transformer-based video encoders are provided to contextualize the proposed approach. Furthermore, we examine robustness to adversarial perturbations, fairness across demographic groups, and policy implications for privacy-preserving surveillance. The paper concludes with a forward-looking perspective on how interleaved multi-scale motion modeling can inform next-generation infrastructure for safe and equitable urban monitoring.
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