How to Cite:
Theophin Davis, "Unified AI Platforms for Real-Time City Surveillance" American International Journal of Computer Science and Technology, Vol. 1, No. 1, pp. 23-31, 2025.

Abstract:
The rapid urbanization of cities has led to increased challenges in maintaining public safety, managing traffic, and preventing criminal activities. Traditional surveillance systems, often fragmented and reactive, struggle to address the dynamic demands of modern urban environments. This paper proposes a unified AI platform architecture that integrates computer vision, edge computing, real-time analytics, and cloud-based data fusion to provide seamless, scalable, and intelligent city surveillance. By consolidating disparate surveillance systems and leveraging advanced AI models, this platform enables proactive threat detection, real-time incident response, and data-driven decision-making. We explore current implementations, key components, technical challenges, and future directions for unified AI-based surveillance systems, highlighting their potential to transform smart city infrastructure while also addressing ethical and privacy concerns.

Keywords: Real-Time Surveillance, Unified AI Platform, Smart City, Edge Computing, Computer Vision, Data Fusion, Public Safety, Privacy, Anomaly Detection, Urban Monitoring.

References:
1. Rose, A., & Chilton, D. (2020). Smart Surveillance in Urban Cities: Applications and Challenges. Urban Tech Journal, 14(3), 110–125.
2. Zhang, L., Chen, Y., & Xu, W. (2019). Edge Computing for Real-Time Surveillance: A Case Study in Traffic Monitoring. IEEE Internet of Things Journal, 6(5), 8382–8390.
3. Jain, A., & Malhotra, R. (2021). Ethical Considerations in AI Surveillance Systems. Journal of Artificial Intelligence Ethics, 7(2), 45–62.
4. Kumar, S., et al. (2020). Unified Surveillance Networks Using AI and IoT for Urban Safety. Sensors and Systems, 18(6), 407–418.
5. Park, J., & Lee, D. (2022). Federated Learning for Privacy-Preserving Surveillance Applications. ACM Transactions on Intelligent Systems, 11(4), 1–23.
6. Al-Kuwaiti, M., & Al-Teneiji, H. (2021). AI-Driven Smart City Solutions: A UAE Perspective. Middle East Journal of Technology, 10(1), 67–80.
7. Singh, P., & Verma, T. (2019). Computer Vision in Public Safety: Opportunities and Risks. IEEE Computer, 52(7), 30–37.
8. Wang, H., Zhang, X., & Sun, Y. (2020). Digital Twins for Smart City Security Management. Future Cities and Environment, 6(1), 1–12.
9. Maras, M. H., & Wandt, A. (2021). Facial Recognition and the Smart City: Technology, Security, and Society. Journal of Surveillance Studies, 8(1), 19–35.
10. Koops, B.-J., & Leenes, R. (2018). Privacy and the Use of Facial Recognition Technologies in Public Spaces. Computer Law & Security Review, 34(3), 421–429.
11. Mahdavinejad, M. S., et al. (2018). Machine Learning for Smart City Applications: Case Studies and Guidelines. Sensors, 18(8), 2670.
12. Smith, A., & Wallace, R. (2020). Building Ethical AI in Surveillance Systems: A Governance Approach. AI & Society, 35(4), 845–858.
13. Tang, J., & Zhao, M. (2021). Autonomous Drones for Surveillance and Emergency Response in Urban Areas. Robotics and Autonomous Systems, 133, 103643.
14. London Metropolitan Police. (2022). Real-Time AI Surveillance in Urban Safety Pilots. Internal Report, London GovTech Summit.
15. Wei, L., et al. (2019). Anomaly Detection in Urban Surveillance Using Deep Learning. Pattern Recognition Letters, 118, 88–95.