ICDSC 2019

ICDSC 2019
13th International Conference on Distributed Smart Cameras
Event in series	ICDSC
Dates	2019/09/09 (iCal) - 2019/09/11
Homepage:	https://event.unitn.it/icdsc2019/#home
Location
Location:	Trento, Italy
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Important dates
Submissions:	2019/05/24
Accepted short papers:	12
Committees
General chairs:	Nicola Conci, Caifeng Shan
PC chairs:	Lucio Marcenaro, Jungong Han
Table of Contents

Smart camera networks are a fundamental piece of our intelligent cities, buildings, and homes, progressively inserting themselves into our lives. From smart surveillance systems composed of a multitude of smart camera nodes to small wearable cameras able to render a visual log of our daily experience, these devices interact with each other and with a wealth of other smart things, through the Internet. Their rapid development has been possible thanks to the convergence of several technologies. From advanced image sensors and vision chips to embedded vision systems capable of efficient feature extraction, image encoding and wireless transmission of the relevant visual content: this technology opens the door to new application domains, where video analytics and semantic information extraction is performed in a distributed fashion, implementing novel cooperative and/or collaborative frameworks for scene understanding. ICDSC is the appropriate forum to discuss recent advances and open issues in these topics. Topics

ICDSC 2019 is seeking original high quality submissions addressing innovative research in the broad field of camera networks. Topics include, but are not limited to:

• Distributed smart cameras and network architectures
• Distributed video analytics
• Data aggregation and information fusion
• Smart cameras in Internet of Things (IoT), Industry 4.0 and smart transportation
• Smart image sensors and vision chips
• Mobile vision
• 3D reconstruction
• Autonomous vehicles and autonomous driving
• Social robots
• Body-worn cameras
• Machine learning in distributed camera networks
• Emerging applications (e.g., smart cities, virtual/augmented reality, health monitoring)
• Deep learning on embedded systems