Abstract
The past few years have seen a dramatic request for semantic video analysis. Object based interpretation in real-time imposes increased challenges on resource management to maintain sufficient quality of service, and requires careful design of the system architecture. This paper focuses on the role of context for system performance in a multi-stage object detection process. We extract context from simple features to determine regions of interest, provide an innovative method to identify the object’s topology from local object features, and we outline the concept for a correspondingly structured system architecture. Performance implications are analysed with reference to the application of logo detection in sport broadcasts and provide evidence for the crucial improvements achieved from context information.
This work is funded by the European Commission’s IST project DETECT under grant number IST-2001-32157.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
J. Assfalg, M. Bertini, C. Colombo, and A. Del Bimbo. Semantic annotation of sports videos. IEEE Multimedia, 9(2):52–60, 2002.
S. Becker. Implicit learning in 3D object recognition: The importance of temporal context. Neural Computation, 11(2):347–374, 1999.
S. Belongie, C. Carson, H. Greenspan, and J. Malik. Color-and texture-based image segmentation using em and its applications to content-based image retrieval. In Proc. International Conference on Computer Vision, pages 675–682. Bombay, India, 1998.
A. Del Bimbo. Visual Information Retrieval. Morgan Kaufmann Publishers, San Francisco, CA, 1999.
F. Bremond and M. Thonnat. A context representation for surveillance systems. In Proc. Workshop on Conceptual Descriptions from Images, 1996.
J. L. Crowley, J. Coutaz, G. Rey, and P. Reignier. Perceptual components for context aware computing. In Proc. 4th International Conference on Ubiquitous Computing, 2002.
A. K. Dey. Understanding and using context. In Proc. 3rd International Conference on Ubiquitous Computing, 2001.
B. A. Draper. Learning control strategies for object recognition. In K. Ikeuchi and M. Veloso, editors, Symbolic Visual Learning, chapter 3, pages 49–76. Oxford University Press, New York, 1997.
D. Hall, C. de Verdiere, and J.L. Crowley. Object recognition using coloured receptive fields. In Proc. European Conference on Computer Vision, 2000.
S. Hongong, F. Bremond, and R. Nevatia. Bayesian framework for video surveillance application. In Proc. International Conference onPattern Recognition, 2000.
M. Irani and P. Anandan. Video indexing based on mosaic representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 86(5):905–921, 1998.
L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11):1254–1259, November 1998.
B. Mel. Seemore: Combining color, shape, and texture histogramming in a neurally-inspired approach to visual object recognition. Neural Computation, 9:777–804, 1997.
A. Mohan, C. Papageorgiou, and T. Poggio. Example-based object detection in images by components. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(4):349–361, 2001.
M.R. Naphade and T.S. Huang. A probabilistic framework for semantic video indexing, filtering, and retrieval. IEEE Transactions on Multimedia, 3(1):141–151, 2001.
S. Obdrzalek and J. Matas. Object recognition using local affine frames on distinguished regions. In Proc. British Machine Vision Conference, 2002.
L. Paletta, G. Paar, and A. Wimmer. Mobile visual detection of traffic infrastructure. In Proc. IEEE International Conference on Intelligent Transportation Systems, pages 616–621, Oakland, CA, 2001.
L. Paletta and A. Pinz. Active object recognition by view integration and reinforcement learning. Robotics and Autonomous Systems, 31(1–2):71–86, 2000.
L. Paletta, M. Prantl, and A. Pinz. Learning temporal context in active object recognition using Bayesian analysis. In Proc. International Conference on Pattern Recognition, pages 695–699, 2000.
F. Sadjadi. Automatic Target Recognition XII. Proc. of SPIE Vol. 4726, Aerosense 2002, Orlando, FL, 2002.
B. Schiele and J. L. Crowley. Transinformation for active object recognition. In Proc. International Conference on Computer Vision, pages 249–254, 1998.
B. Schiele and J.L. Crowley. Recognition without correspondence using multidimensional receptive field histograms. International Journal of Computer Vision, pages 31–50, 2000.
C. Schmid. A structured probabilistic model for recognition. In Proc. IEEE International Conference on Computer Vision, 1999.
A. Torralba and P. Sinha. Statistical context priming for object detection. In Proc. IEEE International Confernce on Computer Vision, 2001.
P. Viola and M. Jones. Rapid object detection using a bossted cascade of simple features. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Paletta, L., Greindl, C. (2003). Context Based Object Detection from Video. In: Crowley, J.L., Piater, J.H., Vincze, M., Paletta, L. (eds) Computer Vision Systems. ICVS 2003. Lecture Notes in Computer Science, vol 2626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36592-3_48
Download citation
DOI: https://doi.org/10.1007/3-540-36592-3_48
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00921-4
Online ISBN: 978-3-540-36592-1
eBook Packages: Springer Book Archive