Visual Sensing for Urban Intelligence

Dear Colleagues,

The focus of this Special Issue is to present the progress in video sensor network technologies and their applications to advance the design, implementation, and management of urban assets. The ultimate goal is to support city administrators and stakeholders in designing sustainable and personalized solutions for the autonomous, reliable, and efficient operation of modern, digital cities.

To this end, the combination of methods from computer vision, artificial intelligence, and pattern recognition with the progress in edge computing and embedded systems appears to have great potential that has not yet been revealed. For instance, smart camera networks, i.e., networks of cameras equipped with high-level visual intelligence, can bring power and flexibility to the city to address wide-area monitoring problems.
Besides active real-time monitoring, smart cameras and video analytics can help in understanding the behavior of people both in public or private spaces and in outdoor/indoor scenarios and how people perceive the environment around them.

With the critical issues raised by the COVID-19 outbreak, special attention will be paid to smart monitoring applications concerned with the adherence to precautions and social distancing along with new, unexplored ideas for the analysis of individuals and clusters of people during the pandemic.

This Special Issue will be of interest to researchers, data scientists, and practitioners working on models for the automatic understanding of visual information and aiming to gain a more in-depth insight into the city for adaptive policy-making. We invite applicants to submit innovative work that is connected, but not limited, to the following fields of interest:

• Intelligent sensors for ecoscaping and sustainable urban development;
• Data processing and analysis methods for the automatic detection of urban-related information;
• Vision understanding applications in smart surveillance and the urban environment;
• Human action recognition from video;
• Decision-making in real urban scenarios;
• Machine learning methods applied to smart surveillance and urban mobility;
• Deep neural network architectures for computer vision;
• Supervised, semi-supervised, and unsupervised learning from video;
• Artificial intelligence in resource-constrained environments and on-device machine learning;
• Embedded systems and edge computing;
• Perception, sensing, and smart environment monitoring;
• Sensor fusion for environment perception;
• Smart sensor software architectures;
• Video data representation, summarization, and visualization.

Dr. Giuseppe Riccardo Leone
Dr. Davide Moroni
Prof. Paolo Remagnino
Guest Editors

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