Real Time Video Understanding Using Deep Learning for Public Surveillance and Safety Analytics
Download PDFAbstract
This chapter explores the transformative impact of deep learning on real-time video understanding for public surveillance and safety analytics. We delve into the foundational concepts, advanced techniques, and practical applications of deep learning models in analyzing vast streams of video data from surveillance cameras. The chapter provides a comprehensive overview of state-of-the-art methodologies, including object detection, tracking, and anomaly detection, which are critical for enhancing public safety. We propose a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short- Term Memory (LSTM) networks for temporal analysis, enabling robust and efficient real-time video understanding. The performance of the proposed methodology is evaluated on a public dataset, demonstrating its effectiveness in identifying and classifying various activities and events in surveillance footage. The chapter concludes with a discussion of the results, challenges, and future directions in this rapidly evolving field.
References
- Joseph Redmon et al. “You only look once: Unified, real-time object detection”. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, pp. 779–788.
- Wei Liu et al. “Ssd: Single shot multibox detector”. In: European conference on computer vision. Springer. 2016, pp. 21–37.
- S Hochreiter and J Schmidhuber. Long short-term memory. Neural Cornputation 9 (8): 1735–1780. 1997.
- LiW X MahadevanV et al. “Anomaly detection in crowded scenes”. In: IEEE computer society conference on computer Vision and Pattern Recognition (2010).
- Karen Simonyan and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition”. In: arXiv preprint arXiv:1409.1556 (2014).
- Archana Chaudhari et al. “Multimodal deep learning framework for real-time women safety surveillance and threat mitigation”. In: Artificial Intelligence and Sustainable Innovation. CRC Press, 2026, pp. 335–341.
- Pedro Lira et al. “Enhancing Situational Awareness in Public Safety with Frame-Accumulated Face Recognition and Distance-Based”. In: Intelligent Systems: 35th Brazilian Conference, BRACIS 2025, Fortaleza-CE, Brazil, September 29–October 2, 2025, Proceedings, Part IV. Springer Nature. 2026, p. 245.
- Sharath Kumar MV, KM Sowmyashree, et al. “AI Driven Real Time Surveillance System for Public Safety”. In: International Journal of Fundamental and Applied Sciences (IJFAS) (2026), pp. 1–8.
- Mrs Priyanka ME et al. “Real-Time Traffic Analysis System Based on Deep Learning”. In: Journal of Advance and Future Research 4.1 (2026), pp. 273–279.
Deep Learning: Foundations, Advances, and Intelligent Applications
