Abstract
An object detection scheme using the Scale Invariant Feature Transform (SIFT) is proposed in this paper. The SIFT extracts distinctive invariant features from images and it is a useful tool for matching between different views of an object. This paper proposes how the SIFT can be used for an object detection problem, especially human detection problem. The Support Vector Machine (SVM) is adopted as the classifier in the proposed scheme. Experiments on INRIA Perdestrian dataset are performed. Preliminary results show that the proposed SIFT-SVM scheme yields promising performance in terms of detection accuracy.
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
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. of CVPR, vol. 1, pp. 886–893 (2005)
Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: Proc. of CVPR, vol. 1, pp. 1–8 (2008)
Zhou, H., Yuan, Y., Shi, Y.: Object tracking using SIFT features and mean shift. Journal on Computer Vision and Image Understanding 113(3), 345–352 (2009)
Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Lowe, D.: Object Recognition from Local Scale-Invariant Features. In: Proc. of IEEE Int. Conf. Computer Vision, vol. 2, pp. 1150–1157 (1999)
Brown, M., Lowe, D.: Invariant Features from Interest Point Groups. In: Proc. of British Machine Vision Conference, vol. 1, pp. 656–665 (2002)
Brown, M., Lowe, D.: Recognising panoramas. In: Proc. of IEEE Int. Conf. Computer Vision, vol. 2, pp. 1218–1225 (2003)
Ahonen, T., Hadid, A., Pietikainen, M.: Face Description with Local Binary Patterns. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)
Novak, C.: Anatomy of a color histogram. In: Proc. of CVPR, vol. 1, pp. 599–605 (1992)
Cortes, C., Vapnik, V.: Support-Vector Networks. Journal on Machine Learning 20(3), 273–297 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Nguyen, T., Park, EA., Han, J., Park, DC., Min, SY. (2014). Object Detection Using Scale Invariant Feature Transform. In: Pan, JS., Krömer, P., Snášel, V. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 238. Springer, Cham. https://doi.org/10.1007/978-3-319-01796-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-01796-9_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01795-2
Online ISBN: 978-3-319-01796-9
eBook Packages: EngineeringEngineering (R0)