Abstract
In this paper, we propose a new approach for keypoint-based object detection. Traditional keypoint-based methods consist of classifying individual points and using pose estimation to discard misclassifications. Since a single point carries no relational features, such methods inherently restrict the usage of structural information. Therefore, the classifier considers mostly appearance-based feature vectors, thus requiring computationally expensive feature extraction or complex probabilistic modelling to achieve satisfactory robustness. In contrast, our approach consists of classifying graphs of keypoints, which incorporates structural information during the classification phase and allows the extraction of simpler feature vectors that are naturally robust. In the present work, 3-vertices graphs have been considered, though the methodology is general and larger order graphs may be adopted. Successful experimental results obtained for real-time object detection in video sequences are reported.
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
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 20, 91–110 (2004)
Bay, H., Tuytelaars, T., van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Lepetit, V., Fua, P.: Keypoint recognition using randomized trees. IEEE Transactions on Pattern Analysis and Machine Inteligence 28, 1465–1479 (2006)
Özuysal, M., Fua, P., Lepetit, V.: Fast keypoint recognition in ten lines of code. In: Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE Computer Society, Los Alamitos (2007)
Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Inteligence 19, 530–535 (1997)
Tell, D., Carlsson, S.: Combining appearance and topology for wide baseline matching. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 68–81. Springer, Heidelberg (2002)
Tang, F., Tao, H.: Object tracking with dynamic feature graph. In: Proceedings of the 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 25–32. IEEE Computer Society, Los Alamitos (2005)
OpenCV: http://opencv.willowgarage.com/
Shi, J., Tomasi, C.: Good features to track. In: Proceedings of the 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 593–600. IEEE Computer Society, Los Alamitos (1994)
Fortune, S.: A sweepline algorithm for Voronoi diagrams. In: Proceedings of the Second Annual Symposium on Computational Geometry, pp. 313–322. ACM, New York (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hashimoto, M., Cesar, R.M. (2009). Object Detection by Keygraph Classification. In: Torsello, A., Escolano, F., Brun, L. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2009. Lecture Notes in Computer Science, vol 5534. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02124-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-02124-4_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02123-7
Online ISBN: 978-3-642-02124-4
eBook Packages: Computer ScienceComputer Science (R0)