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Combining shape-based and gradient-based classifiers for vehicle classification

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Conference Paper (401.0Kb)

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info:eu-repo/semantics/openAccess

Date

2015-10

Author

Karaimer, Hakkı Can
Çınaroğlu, İbrahim
Baştanlar, Yalın

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Citation

Karaimer, H. C., Çınaroğlu, İ., and Baştanlar, Y. (2015, September). Combining shape-based and gradient-based classifiers for vehicle classification. Paper presented at the 18th IEEE International Conference on Intelligent Transportation Systems. doi:10.1109/ITSC.2015.135

Abstract

In this paper, we present our work on vehicle classification with omnidirectional cameras. In particular, we investigate whether the combined use of shape-based and gradient-based classifiers outperforms the individual classifiers or not. For shape-based classification, we extract features from the silhouettes in the omnidirectional video frames, which are obtained after background subtraction. Classification is performed with kNN (k Nearest Neighbors) method, which has been commonly used in shape-based vehicle classification studies in the past. For gradient-based classification, we employ HOG (Histogram of Oriented Gradients) features. Instead of searching a whole video frame, we extract the features in the region located by the foreground silhouette. We use SVM (Support Vector Machines) as the classifier since HOG+SVM is a commonly used pair in visual object detection. The vehicle types that we worked on are motorcycle, car and van (minibus). In experiments, we first analyze the performances of shape-based and HOG-based classifiers separately. Then, we analyze the performance of the combined classifier where the two classifiers are fused at decision level. Results show that the combined classifier is superior to the individual classifiers. © 2015 IEEE.

Source

18th IEEE International Conference on Intelligent Transportation Systems

Volume

2015

URI

http://doi.org/10.1109/ITSC.2015.135
http://hdl.handle.net/11147/5492

Collections

  • Computer Engineering / Bilgisayar Mühendisliği [243]
  • Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection [4673]
  • WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection [4803]



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