Research Article

An Approach for Detection of Entities in Dynamic Media Contents

Authors

  • 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).

Article information

Journal

Journal of Computer Science and Technology Studies

Volume (Issue)

5 (3)

Pages

13-24

Published

2023-07-11

How to Cite

Nzakiese, M., & Armando, N. (2023). An Approach for Detection of Entities in Dynamic Media Contents. Journal of Computer Science and Technology Studies, 5(3), 13–24. https://doi.org/10.32996/jcsts.2023.5.3.2

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Keywords:

Entity Detection, Computer Vision, Multi-instance, Public Safety