Activity Monitoring by Multiple Distributed Sensing

Dear Colleagues,

Visual sensor networks are now progressively becoming a fundamental piece of our lives, with a wide application in smart cities, traffic monitoring, and crime event detection.

The increasing computational power, together with the decreasing costs of dedicated hardware and the availability of communications infrastructures are greatly modifying the traditional approaches to visual monitoring. What was previously done at the level of individual sensors, today is done via sensor networks. The focus of the developed approaches is lightly changed, pointing not only on the robustness of algorithms and the relative results, but also on the computational time and the communications between sensors.

Activity monitoring, that crosses different aspects of intelligent systems development, was typically threated in a single-view context; the multi-view approach has been also investigated, but in scenarios where the interaction between nodes was very limited and typically demanded to a central processing unit, devoted to merging and decision making tasks.

Now the attention of researchers is on the sharing and distribution of the information, as well as in the communication and processing. In such a context, there are now many challenges to face in order to perform activity recognition in a distributed manner, spacing from the design of the network, to the automatic integration of new devices.

Aim of this Special Issue is to bring together researchers from different communities (such as Computer Vision, networked embedded sensing, artificial intelligence and so on) which address the problem of interpretation of the information coming from multiple distributed sensing systems.

Topics of interest include, but are not limited to, the following:

• Single and multiple object tracking;
• Multi-agent/multi sensing activity detection and recognition;
• Re-identification;
• Analysis of communication issues in sensor networks;
• Fast real-time detection, segmentation and classification of objects;
• Deep learning solutions for sensor networks applications;
• Human behavior analysis;
• Sensor networks architectures and calibration;
• Scene understanding;
• Event detection;
• Fusion Processing for heterogeneous sensor networks;
• Applications (Video Surveillance, Sport scene analysis, Human Computer Interaction …).

Each manuscript will be blind reviewed by journal academic editors.

Dr. Paolo Spagnolo
Dr. Pier Luigi Mazzeo
Guest Editors

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• Sensor Networks
• Activity Monitoring
• Artificial Intelligence
• Computer Vision
• Deep Learning