Domain Adaptation based Data Mining for Robust Cross-Domain Surveillance Analytics: A Review and Conceptual Framework

Abstract

Cross-domain variability remains a critical challenge in surveillance analytics, where models trained in one environment often fail to generalize effectively to new deployment settings due to domain shift caused by variations in lighting, camera viewpoints, crowd density, and scene dynamics. Traditional surveillance data mining approaches assume identical data distributions between training and testing environments, which is unrealistic in large-scale real-world systems.

This paper presents a comprehensive systematic review and unified conceptual framework for domain adaptation–based surveillance data mining. The study categorizes existing domain adaptation techniques into feature distribution alignment, adversarial learning, self-supervised adaptation, and multi-scene transfer mechanisms. A formal problem formulation is introduced to define source–target distribution mismatch and its impact on surveillance tasks such as anomaly detection and activity recognition.

Furthermore, we propose an integrated domain adaptation framework incorporating domain invariant feature learning and distribution alignment strategies to improve cross-domain robustness while minimizing target-domain annotation requirements. Comparative analysis reveals consistent performance improvements when adaptation mechanisms are employed. The paper also identifies key research challenges including label scarcity, real-time adaptation, scalability, interpretability, and privacy-preserving learning.

This work provides a structured research roadmap toward scalable, annotation-efficient, and generalizable surveillance analytics systems.