An Approach for Detection of Entities in Dynamic Media Contents

Mbongo Nzakiese, Ngombo Armando
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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).
动态媒体内容中实体的一种检测方法
学习的概念几乎是智能代理每一次进化的基础。在本文中,我们提出了一种搜索和检测视频序列中给定实体的方法。具体来说,我们研究了人工神经网络的深度学习技术如何使我们能够检测视频序列中的角色。考虑到所分析的数据中存在的大量对象,在视频中检测字符的技术是一个复杂的研究领域。从获得的结果中,与目前的技术水平相比,我们强调了以下几点:在我们的方法中,在计算机视觉领域,监督学习算法的结构使我们能够从目标字符的简单特征中获得一些成功。我们的研究结果表明,这种新方法使我们能够以一种有效的方式从私人或公共形象库中找到想要的个人。就安哥拉而言,我们提出的分类器为根据目标个人(失踪者、罪犯等)的数据库和综合公共安全中心(CISP)的视频序列加强国家安全系统提供了可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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