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V3SC 2025 : Video Surveillance Systems in Smart Cities: Synthetic Images and Foundation Models for Advanced Monitoring Technologies | |||||||||||||||
Link: https://sites.google.com/view/v3sc-iciap2025 | |||||||||||||||
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Call For Papers | |||||||||||||||
As urban areas grow and evolve into smart cities, the need for advanced, integrated surveillance systems becomes critical for ensuring public safety and efficient city management. This workshop aims to explore the latest technologies and methodologies for creating a cohesive surveillance ecosystem that leverages multiple data sources that can have a powerful impact on different applications, such as monitoring the crowd flow, monitoring traffic, real-time anomaly detection, behavioural analysis, fire detection and others utilising both high-altitude (from drones and satellites) and ground-level images and videos. A further aim of this workshop is to promote the study and development of this field to identify limitations, open issues and further research directions.
The workshop will focus on the integration and deployment of various surveillance technologies in smart cities. This includes traditional CCTV cameras, high-altitude systems such as drones and satellites, and advanced monitoring systems incorporating IoT sensors and AI analytics. We invite original research papers, case studies, and technical reports on (but not limited to) the following topics: Integration of traditional CCTV systems with high-altitude surveillance technologies (drones and satellites) Innovations in urban monitoring using drone and satellite imagery AI and machine learning applications in smart city surveillance Data fusion and analytics for enhanced urban security Privacy and ethical implications of widespread surveillance Case studies of real-world implementations of urban surveillance Real-time monitoring: applications in traffic management and anomaly detection (e.g., fires, critical events) Future trends and challenges in urban surveillance Use of synthetic images for training and testing surveillance algorithms Foundation models and their applicability in video surveillance and analysis Challenges and opportunities in using synthetic data to reduce bias and improve AI model accuracy |
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