Smart cameras combine video sensing, processing, and communication on a single embedded platform. Networks of smart cameras are real-time distributed embedded systems that perform computer vision using multiple cameras. This new approach has emerged thanks to a confluence of simultaneous advances in four key disciplines: computer vision, image sensors, embedded computing, and sensor networks. Recently these visual sensor networks have gained a lot of interest in research and industry; applications include surveillance, assisted living and smart environments.

Although the distribution of sensing and processing in smart camera networks introduces several complications, we believe that the problems it solves are much more important than the challenges of designing and building such a network. This tutorial introduces the underlying concepts of smart camera networks and presents selected applications. It covers topics such as architecture of smart cameras, embedded camera platforms, coordination and control of processing in smart camera networks, visual sensor networks and various applications.

Aim of the Tutorial

The motivation for this tutorial is to bring together researchers and students working on the various fields related to smart camera networks and to introduce this topic to embedded systems people. This half-day tutorial provides a unique opportunity to get introduced to the state-of-the-art and open problems in smart camera networks and to get to know the work and the people conducting research in this field. This tutorial should leverage a fruitful exchange of ideas and stimulate future research among the smart camera and embedded systems communities.

Course Material

Please note that this is a preliminary version of the course material. It will be revised throughout the following weeks.

1. Introduction

  • Motivation and Overview
Part 1 (PDF)

2. Smart cameras

  • Architecture of Smart Cameras
  • Prototypes
Part 2 (PDF)

3. Visual Sensor Networks

  • Advantages and Challenges
  • Characteristics of VSN
  • Research Directions
Part 3 (PDF)

4. Applications

  • Security- and privacy-awareness in Smart Camera Networks
  • Aerial Visual Sensor Networks
Part 4 (PDF)

5. Conclusion

  • Research Challenges
  • Summary
Part 5 (PDF)

Interesting Links


Bernhard Rinner is a Full Professor and chair of pervasive computing at Klagenfurt University (Austria) where he is currently serving as Vice Dean of the Faculty of Technical Sciences.

He received both his PhD and MSc in Telematics from Graz University of Technology in 1996 and 1993, respectively. Before joining Klagenfurt he was with Graz University of Technology and held research positions at the Department of Computer Sciences at the University of Texas at Austin in 1995 and 1998/99. His current research interests include embedded computing, embedded video and computer vision, sensor networks and pervasive computing. He has authored and co-authored more than 120 papers for journals, conferences and workshops, has led many research projects and has served as reviewer, program committee member, program chair and editor-in-chief.

Prof. Rinner has been co-founder and general chair of the ACM/IEEE International Conference on Distributed Smart Cameras and has served as chief editor of a special issue on this topic in the Proceedings of the IEEE. He is Associate Editor of the EURASIP Journal on Embedded Systems and has served as Guest Editor for special issues in the IEEE Journal on Selected Topics in Signal Processing, the Elektrotechnik&Informationstechnik Journal and the EURASIP Journal on Embedded Systems. Together with partners from four European universities he has jointly initiated the Erasmus Mundus Joint Doctorate Program on Interactive and Cognitive Environments (ICE) which is currently the only joint doctoral school funded by the European Commission in the field of ICT. Prof. Rinner is co-chairing the IEEE International Conference on Advanced Video and Signal-Based Surveillance in 2011. He is member of the IEEE, IFIP and TIV (Telematik Ingenieurverband).