Distributed Vision Processing in Smart Camera Networks
CVPR 2007 Short Course
June 18, 2007
Hamid Aghajan (Stanford University, USA)
François Berry (Univ. Blaise Pascal, France)
Horst Bischof (TU Graz, Austria)
Richard Kleihorst (NXP Research, Netherlands)
Bernhard Rinner (Klagenfurt University, Austria)
Wayne Wolf (Princeton University, USA)
Distributed smart cameras combine techniques from computer vision, distributed processing, and embedded computing. Technological advances in the design of sensors and processors have facilitated the development of efficient embedded vision-based techniques. Distributed algorithms can provide more confident deductions about the events of interest or reduce ambiguities in a view caused by occlusion or other factors. Since they operate in real time, a variety of smart environment applications can be enabled based on the development of efficient architectures and algorithms for distributed vision networks.
Building upon the premise of distributed vision-based sensing and processing, ambient intelligence can be conceived as electronic environments that are aware of and responsive to the presence of people. Most application development efforts based on vision have focused towards monitoring scenes and persons. Distributed image sensing networks not only enhance the performance and reliability of such applications, they also enable novel ambient intelligence application areas in which the network provides useful information services to the users by monitoring the events and context they are involved with. This provides a lively field for vision-based research, pushing technology and relevant applications in smart homes, offices, factories, as well as entertainment and gaming application domains.
The target audience consists of researchers across the various vision-based interpretive applications such as human presence and gesture analysis, as well as graduate students involved in vision-based algorithm design research. The course offers a perspective of the various methodologies based on the flexibilities and tradeoffs introduced by distributed vision-based processing.
Topic covered in the course consist of the architecture, data processing and fusion mechanisms, algorithms, and applications of smart camera networks.