Robust baggage detection and classification based on local tri-directional pattern
| Autor: | N.A. Shahbano, Kashif Inayat, Muhammad Abdullah |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Cryptography and Security Computer science business.industry Computer Networks and Communications Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Pattern recognition Artificial intelligence business Cryptography and Security (cs.CR) Computer Science Applications |
| Zdroj: | International Journal of Internet Technology and Secured Transactions. 12:91 |
| ISSN: | 1748-5703 1748-569X |
| DOI: | 10.1504/ijitst.2022.121420 |
| Popis: | 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, 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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