Title: Robust baggage detection and classification based on local tri-directional pattern

Authors: Shahbano; Muhammad Abdullah; Kashif Inayat

Addresses: Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan ' Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan ' Department of Electronics Engineering, Incheon National University, Incheon, South Korea

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.

Keywords: carrying baggage detection and classification; local tri-directional pattern; support vector machine; SVM; boosting machine; video surveillance.

DOI: 10.1504/IJITST.2022.121420

International Journal of Internet Technology and Secured Transactions, 2022 Vol.12 No.2, pp.91 - 102

Received: 10 Sep 2020
Accepted: 29 Jan 2021
Published online: 11 Mar 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article