This article proposes an integrated system for segmentation and classification of two moving objects, including car and pedestrian from their side-view in a video sequence. Based on the use of grey-level co-occurrence matrix (GLCM) in Haar wavelet transformed space, the authors calculated features of texture data from different sub-bands separately. Haar wavelet transform is chosen because the resulting wavelet sub-bands are strongly affecting on the orientation elements in the GLCM computation. To evaluate the proposed method, the results of different sub-bands are compared with each other. Extracted features of objects are classified by using a support vector machine (SVM). Finally, the experimental results showed that use of three sub-bands of wavelets instead of two sub-bands is more effective and has good precision.
Using GLCM features in Haar wavelet transformed space for moving object classification
IET Intelligent Transport Systems ; 13 , 7 ; 1148-1153
2019-04-05
6 pages
Article (Journal)
Electronic Resource
English
image texture , different sub-bands , Haar , extracted features , GLCM features , moving objects , car , object classification , feature extraction , pedestrian , video sequence , Haar transforms , wavelet transforms , image classification , segmentation , texture data , wavelets , support vector machines , GLCM computation , matrix algebra , co-occurrence matrix , experimental results , orientation elements , integrated system , resulting wavelet sub-bands
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