In smart cities, video surveillance is essential for public safety, evolving beyond simple camera installations and centralized monitoring due to the overwhelming amount of footage that challenges human operators. To enhance anomaly detection, experts have developed sophisticated computer vision techniques that classify events as normal or abnormal.
Smart Public Safety Video Surveillance System explores an end-to-end urban video surveillance system, which aims to address asymmetric threats through three key strategies: firstly, it employs a corrective signal called “task-specific QoE” that considers contextual factors; secondly, it utilizes machine learningdriven predictive systems and a method known as "similarity-based meta-reinforcement learning" for effective anomaly detection; and thirdly, it advocates for "zero-touch" self-management systems based on autonomous computing. This holistic approach ensures rapid adaptation and situational awareness, effectively meeting the demands of modern businesses and enhancing overall safety in dynamic urban environments.
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Abhishek Djeachandrane is a research scientist at Airbus Defence and Space’s AI Connectivity Lab, France. His research interests include AI, data science, computer networks, QoE and trustworthy systems.
Said Hoceini is Associate Professor and Head of the N&T Department at IUT CV.UPEC, France. His research focuses on routing algorithms, QoS/QoE, and bioinspired artificial intelligence approaches.
Serge Delmas is an engineer at Airbus Defence and Space, Secure Land Communications, France. He leads the Research & Technology Projects and Innovations team, driving cutting-edge solutions to enhance future emergency services.
Abdelhamid Mellouk is Full-time University Professor, Director of the IT4H High School Engineering Department and Head of the TincNET Research Team, UPEC, France. He is also the founder of Network Control Research and Curricula activities at UPEC, President of the Policies and Programs commission at the National Council for Scientific Research and Technologies, a HCERES Expert, a CNU member and Co-President of the DS-AI Systematic Deep Tech Hub.
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Hardcover. Condición: new. Hardcover. In smart cities, video surveillance is essential for public safety, evolving beyond simple camera installations and centralized monitoring due to the overwhelming amount of footage that challenges human operators. To enhance anomaly detection, experts have developed sophisticated computer vision techniques that classify events as normal or abnormal. Smart Public Safety Video Surveillance System explores an end-to-end urban video surveillance system, which aims to address asymmetric threats through three key strategies: firstly, it employs a corrective signal called task-specific QoE that considers contextual factors; secondly, it utilizes machine learningdriven predictive systems and a method known as "similarity-based meta-reinforcement learning" for effective anomaly detection; and thirdly, it advocates for "zero-touch" self-management systems based on autonomous computing. This holistic approach ensures rapid adaptation and situational awareness, effectively meeting the demands of modern businesses and enhancing overall safety in dynamic urban environments. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781836690542
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