Difference between revisions of "ICDSC 2019"
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|has Proceedings Link=https://dl.acm.org/doi/proceedings/10.1145/3349801 | |has Proceedings Link=https://dl.acm.org/doi/proceedings/10.1145/3349801 | ||
|Acronym=ICDSC 2019 | |Acronym=ICDSC 2019 | ||
− | |End date=2019 | + | |End date=2019-09-11 |
|Series =ICDSC | |Series =ICDSC | ||
|Type =Conference | |Type =Conference | ||
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|State =IT/32 | |State =IT/32 | ||
|City =IT/32/Trento | |City =IT/32/Trento | ||
+ | |Year =2019 | ||
|Homepage=https://event.unitn.it/icdsc2019/#home | |Homepage=https://event.unitn.it/icdsc2019/#home | ||
− | |Start date=2019 | + | |Start date=2019-09-09 |
|Title =13th International Conference on Distributed Smart Cameras | |Title =13th International Conference on Distributed Smart Cameras | ||
− | |Accepted papers=24}} | + | |Accepted papers=24 |
+ | }} | ||
Latest revision as of 04:12, 6 December 2021
Event Rating
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List of all ratings can be found at ICDSC 2019/rating
ICDSC 2019 | |
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13th International Conference on Distributed Smart Cameras
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Event in series | ICDSC |
Dates | 2019-09-09 (iCal) - 2019-09-11 |
Homepage: | https://event.unitn.it/icdsc2019/#home |
Location | |
Location: | IT/32/Trento, IT/32, IT |
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 | |
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