Combining Shape-Based and Gradient-Based Classifiers for Vehicle Classification

In this paper, the authors present their work on vehicle classification with omnidirectional cameras. In particular, the authors investigate whether the combined use of shape-based and gradient-based classifiers outperforms the individual classifiers or not. For shape-based classification, the authors 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, the authors employ HOG (Histogram of Oriented Gradients) features. Instead of searching a whole video frame, the authors extract the features in the region located by the foreground silhouette. The authors use SVM (Support Vector Machines) as the classifier since HOG+SVM is a commonly used pair in visual object detection. The vehicle types that the authors worked on are motorcycle, car and van (minibus). In experiments, the authors first analyze the performances of shape-based and HOG-based classifiers separately. Then, the authors 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.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 800-805
  • Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

Filing Info

  • Accession Number: 01600934
  • Record Type: Publication
  • ISBN: 9781467365956
  • Files: TRIS
  • Created Date: May 2 2016 3:21PM