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.


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    Title :

    Using GLCM features in Haar wavelet transformed space for moving object classification


    Contributors:

    Published in:

    Publication date :

    2019-04-05


    Size :

    6 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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