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Obstacle categorization based on hybridizing global and local features

Title
Obstacle categorization based on hybridizing global and local features
Authors
Woo, Jeong-WooLim, Young-ChulLee, Minho
DGIST Authors
Lim, Young-Chul
Issue Date
2009
Citation
16th International Conference on Neural Information Processing, ICONIP 2009, 5864 LNCS(PART 2), 1-10
Type
Conference
Article Type
Conference Paper
ISBN
364210682X
ISSN
0302-9743
Abstract
We propose a novel obstacle categorization model combining global feature with local feature to identify cars, pedestrians and unknown backgrounds. A new obstacle identification method, which is hybrid the global feature and local feature, is proposed for robustly recognizing an obstacle with and without occlusion. For the global analysis, we propose the modified GIST based on biologically motivated the C1 feature, which is robust to image translation. We also propose the local feature based categorization model for recognizing partially occluded obstacle. The local feature is composed of orientation information at a salient position based on the C1 feature. A classifier based on the Support Vector Machine (SVM) is designed to classify these two features as cars, pedestrians and unknown backgrounds. Finally, all classified results are combined. Mainly, the obstacle categorization model makes a decision based on the global feature analysis. Since the global feature cannot express partially occluded obstacle, the local feature based model verifies the result of the global feature based model when the result is an unknown background. Experimental results show that the proposed model successfully categorizes obstacles including partially occluded obstacles. © 2009 Springer-Verlag Berlin Heidelberg.
URI
http://hdl.handle.net/20.500.11750/3968
DOI
10.1007/978-3-642-10684-2_1
Publisher
Springer
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Future Automotive Technology2. Conference Papers


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