Open AccessUsing GLCM features in Haar wavelet transformed space for moving object classification
Author(s) -
Kiaee Nadia,
Hashemizadeh Elham,
Zarrinpanjeh Nima
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5192
Subject(s) - artificial intelligence , haar , haar wavelet , pattern recognition (psychology) , wavelet , support vector machine , computer vision , discrete wavelet transform , computer science , wavelet transform , segmentation , object (grammar) , grey level , image (mathematics)
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.