Smart camera networks are real-time distributed embedded systems that perform computer vision using multiple cameras. They have emerged thanks to the simultaneous advances in four key disciplines: computer vision, image sensors, embedded computing, and sensor networks.
“We aim at advancing this field of research by applying novel networking concepts as well as by developing various prototypes,” Bernhard Rinner explains. Analyzing the captured data onboard the cameras and within the network in real-time is important to avoid transferring large volume of video data over the network. The strong resource limitations are challenging and require efficient algorithms and network management. The research team has developed various camera platforms and has deployed them in indoor and outdoor environments. Test applications include traffic monitoring, environmental monitoring, and surveillance.
In-network processing is concerned about the management of the available resources in the camera network. Examples of such resource management include clustering, i.e., selecting a group of cameras which jointly work a specific task, handover, i.e., transfering a specific task from one camera to another, and calibration, i.e., estimating the spatial relationship among the individual cameras.
In her recent work, Jennifer Simonjan developed a decentralized and resource-aware algorithm for estimating the poses of all camera nodes without any user interaction. “Self-calibration is achieved in two steps,” she explains. “First, overlapping camera pairs estimate relative positions and orientations by exchanging locally measured distances and angles to detected objects. Second, calibration information of overlapping cameras is spread throughout the network such that poses of non-overlapping cameras can also be estimated.”
Bernhard Rinner and his team have been involved in smart camera research for more than 15 years. The following list documents some highlights of that period:
- Co-initiation of the ACM/IEEE International Conference on Distributed Smart Cameras (13th edition in 2019)
- Co-editing (with W. Wolf) of a special issue on Distributed Smart Cameras in the Proceedings of the IEEE
- Various tutorial talks on smart cameras (e.g. Smart Cameras and Visual Sensor Networks at the S5 spring school in Modena
- Co-editing (with M. Reisslein and A. Roy-Chowdhury) of a special issue on Smart Camera Networks in IEEE Computer
- Browse the relaunched first SmartCam website (@TUGraz) (now bernhardrinner.com/smartcam) and watch out for our first videos!
- Intelligent Vision Austria (ComVis)
Co-principal investigator. Funding from Federal Ministry of Research and Austrian Institute of Technology, 2014-2019
- Cooperative, Resource-Optimization and Self-Organization in Mobile, Mixed-Reality Environments (CROSMOS)
Principal investigator. KWF, 2014-2015
- Self-organizing Multimedia Architecture (SOMA)
Co-principal investigator. Funding from EU/KWF/BABEG, 2009-2012
- Closed-Loop Integration of Cognition, Communication and Control (CLIC)
Co-Principal investigator. Funding from FFG, 2009-2010
- Autonomous Traffic Monitoring by Embedded Vision (EVis)
Principal investigator. Funding from FFG, 2007-2010
- Jennifer Simonjan and Bernhard Rinner. Decentralized and Resource-efficient Self-Calibration of Visual Sensor Networks. Ad Hoc Networks, 2019.
- C. Piciarelli, L. Esterle, A. Khan, B. Rinner, and G. L. Foresti. Dynamic reconfiguration in camera networks: A short survey. IEEE Transactions on Circuits and Systems for Video Technology, 2016.
- B. Rinner, L. Esterle, J. Simonjan, G. Nebehay, R. Pflugfelder, P. R. Lewis, and G. F. Dominguez. Self-aware and self-expressive camera networks. IEEE Computer, 2015.
- C. Micheloni, B. Rinner, and G. L. Foresti. Video analysis in PTZ camera networks: From master-slave to cooperative smart cameras. IEEE Signal Processing Magazine, 2010.
- B. Rinner and W. Wolf. Introduction to distributed smart cameras. In Proceedings of the IEEE, 2008.
- M. Bramberger, A. Doblander, A. Maier, B. Rinner, and H. Schwabach. Distributed embedded smart cameras for surveillance applications. IEEE Computer, 2006.