Multimedia data is rapidly gaining importance along with recent developments such as the increasing deployment of surveillance cameras in public locations. In a few years time, analyzing the content of multimedia data will be a problem of phenomenal proportions, as digital video may produce data at rates beyond 100 Mb/s, and multimedia archives steadily run into Petabytes of storage space. Consequently, for urgent problems in multimedia content analysis, Grid computing is rapidly becoming indispensable. In this video demonstration we show the viability of widearea Grid systems in adhering to the heavy demands of a realtime object recognition task. Specifically, we show a Sony Aibo robot dog, capable of recognizing objects from a set of learned objects, while connected to a Grid system comprising of cluster computers located in Europe, the United States, and Australia. As such, we demonstrate the effective integration of state-of-the-art results from two largely distinct research fields: multimedia content analysis and Grid computing. See also: http://www.science.uva.nl/~fjseins/aibo.html.
[1]
Arnold W. M. Smeulders,et al.
Color Invariance
,
2001,
IEEE Trans. Pattern Anal. Mach. Intell..
[2]
Dennis Koelma,et al.
A software architecture for user transparent parallel image processing
,
2002,
Parallel Comput..
[3]
Arnold W. M. Smeulders,et al.
Color constancy from physical principles
,
2003,
Pattern Recognit. Lett..
[4]
Arnold W. M. Smeulders,et al.
The Amsterdam Library of Object Images
,
2004,
International Journal of Computer Vision.
[5]
Dennis Koelma,et al.
User transparency: a fully sequential programming model for efficient data parallel image processing
,
2004,
Concurr. Pract. Exp..
[6]
Dennis Koelma,et al.
Finite state machine-based optimization of data parallel regular domain problems applied in low-level image processing
,
2004,
IEEE Transactions on Parallel and Distributed Systems.
[7]
J. Call,et al.
Word Learning in a Domestic Dog: Evidence for "Fast Mapping"
,
2004,
Science.