An Approach for Detection of Entities in Dynamic Media Contents

TL;DR

This paper introduces a deep learning-based method for detecting specific entities, such as characters, in video sequences, with applications in security and media analysis, demonstrating improved efficiency over existing techniques.

Contribution

The paper presents a novel deep learning approach using supervised learning algorithms for entity detection in videos, enhancing accuracy and efficiency in identifying target individuals.

Findings

Effective detection of characters in video sequences.

Potential applications in security systems.

Improved accuracy over previous methods.

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 neuralnetworks 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).