Robust baggage detection and classification based on local tri-directional pattern
by Shahbano; Muhammad Abdullah; Kashif Inayat
International Journal of Internet Technology and Secured Transactions (IJITST), Vol. 12, No. 2, 2022

Abstract: In recent decades, the automatic video surveillance system has gained significant importance in computer vision community. The crucial objective of surveillance is monitoring and security in public places. In the traditional local binary pattern, the feature description is somehow inaccurate, and the feature size is large enough. Therefore, to overcome these shortcomings, our research proposed a detection algorithm for a human with or without carrying baggage. The local tri-directional pattern descriptor is exhibited to extract features of different human body parts including head, trunk, and limbs. Then with the help of support vector machine (SVM), extracted features are trained and evaluated. Experimental results on INRIA and MSMT17_V1 datasets show that LtriDP outperforms several state-of-the-art feature descriptors and validate its effectiveness.

Online publication date: Fri, 11-Mar-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 Internet Technology and Secured Transactions (IJITST):
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