Author
Listed:
- Mbongo Nzakiese
(Department of Informatics Engineering, Instituto de Tecnologia de Informação e Comunicação, Universidade de Luanda, Luanda, Angola)
- Ngombo Armando
(Department of Research, Innovation, Entrepreneurship & Post-graduate activities, Universidade Rainha Njinga a Mbande, Malanje-Angola; Department of Informatics Engineering, Instituto Politécnico da Universidade Kimpa Vita, Uíge, Angola)
Abstract
The notion of learning underlies almost every evolution of Intelligent Agents. In this paper, we present an approach for searching and detecting a given entity in a video sequence. Specifically, we study how the deep learning technique by artificial neural networks allows us to detect a character in a video sequence. The technique of detecting a character in a video is a complex field of study, considering the multitude of objects present in the data under analysis. From the results obtained, we highlight the following, compared to state of the art: In our approach, within the field of Computer Vision, the structuring of supervised learning algorithms allowed us to achieve several successes from simple characteristics of the target character. Our results demonstrate that is new approach allows us to locate, in an efficient way, wanted individuals from a private or public image base. For the case of Angola, the classifier we propose opens the possibility of reinforcing the national security system based on the database of target individuals (disappeared, criminals, etc.) and the video sequences of the Integrated Public Security Centre (CISP).
Suggested Citation
Mbongo Nzakiese & Ngombo Armando, 2023.
"An Approach for Detection of Entities in Dynamic Media Contents,"
Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 5(3), pages 13-24, July.
Handle:
RePEc:alk:jcsts0:v:5:y:2023:i:3:id:5548
DOI: 10.32996/jcsts.2023.5.3.2
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:alk:jcsts0:v:5:y:2023:i:3:id:5548. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Al-Kindi Center for Research and Development (email available below). General contact details of provider: https://al-kindipublisher.com/index.php/jcsts .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.