Autonomous approach for moving object detection and classification in road applications
by Imane El Manaa; Fadwa Benjelloun; My Abdelouahed Sabri; Ali Yahyaouy; Abdellah Aarab
International Journal of Computer Aided Engineering and Technology (IJCAET), Vol. 18, No. 1/2/3, 2023

Abstract: Our paper presents robust approaches for all moving object detection processes. First of all, we propose an automatic and non-parametric method in the segmentation phase based on Delaunay triangulation applied to the image histogram. For the feature extraction phase, we proceed by the GLCM technique for textural feature extraction and the HSV histogram method for the colour feature extraction. Those features will be used as input of the support vector machine (SVM) algorithm to design a robust classification model that will be used to differentiate between moving and static objects. Thus, static objects will be considered as a part of background, and in the other hand moving objects are surrounded by a bounding box in furtherance of careful tracking.

Online publication date: Mon, 19-Dec-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Aided Engineering and Technology (IJCAET):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com