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Intelligent Video Surveillance: A Systematic Review of Deep Learning Architectures, Multimodal Fusion, and the Emerging Role of Conversational AI (2018–2025)

  • Mittapally Sushmitha *,   
  • Nimmala Pravalika,   
  • Kommaraju Laasya,   
  • Kadiyala Ramana *

Received: 03 Feb 2026 | Revised: 04 Jun 2026 | Accepted: 22 Jun 2026 | Published: 03 Jul 2026

Abstract

The rapid proliferation of urban surveillance infrastructure and the exponential growth of large-scale video data have intensified demand for automated, adaptive monitoring solutions. Recent deep learning advances have transformed conventional surveillance from rule-based systems into adaptive, context-aware frameworks capable of complex spatiotemporal activity detection, classification, and interpretation. This paper presents a comprehensive review of seventy-three deep-learning-based research studies on video anomaly detection (VAD) and human activity recognition published between 2018 and 2025. A systematic categorization of the surveyed works is performed with respect to ten major model families: CNNs for spatial feature extraction recurrent architectures (LSTM, GRU, Bi-LSTM) for temporal reasoning; 3D-CNN and spatiotemporal models for motion encoding autoencoder and generative adversarial frameworks for unsupervised reconstruction transformer and attention-based models for long-range dependency modeling memory-augmented networks for prototypeconstrained normality learning multimodal fusion architectures and edge-intelligent and conversational AI systems for scalable, interactive deployment. The results demonstrate a performance evolution from early CNN-based classifiers (around 85% AUC) to recent transformer-driven and memory-augmented methods achieving AUC values above 97% on UCF-Crime, ShanghaiTech, CUHK Avenue, and RWF-2000. The review additionally incorporates the MSAD multi-scenario benchmark and the CUVA causation dataset, and provides a methodological caveat on the comparability of AUC scores across heterogeneous supervision paradigms. Key open challenges—dataset imbalance, occlusion, illumination variation, domain generalization, and real-time latency—are mapped to research directions including weakly supervised MLLMs, privacy-preserving federated learning, and edge-optimized transformer pipelines.

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DOI
10.53941/aiet.2026.100008
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Volume 3, Issue 2
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Sushmitha, M.; Pravalika, N.; Laasya, K.; Ramana, K. Intelligent Video Surveillance: A Systematic Review of Deep Learning Architectures, Multimodal Fusion, and the Emerging Role of Conversational AI (2018–2025). Artificial Intelligence and Emerging Technologies 2026, 3 (2), 8. https://doi.org/10.53941/aiet.2026.100008.
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