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Application of Attention Mechanism with Bidirectional Long Short-Term Memory (BiLSTM) and CNN for Human Conflict Detection using Computer Vision

15/Dec/2025

Autor: Erick da Silva Farias

Curso: Especialização em Aprendizado de Máquina (IFAM)

Orientador: Prof. Me. Eduardo Palhares Júnior


Apresentação Internacional

  • Evento: III Conferência Internacional de Políticas Públicas e Ciência de Dados
  • Local: Universidade de Aveiro, 3810-193 Aveiro, Portugal
  • Data: 20 de Maio de 2025


Abstract

This study investigates the integration of advanced deep learning techniques, including Attention Mechanism, Convolutional Neural Networks (CNNs), and Bidirectional Long Short-Term Memory (BiLSTM), to improve the detection of violent behaviors in videos. The research explores how the use of the attention mechanism can help focus on the most relevant parts of the video, enhancing the accuracy and robustness of the model. The experiments indicate that the combination of CNNs with BiLSTM and the attention mechanism provides a promising solution for conflict monitoring, offering insights into the effectiveness of different strategies.

Keywords: Computer Vision, Human Conflict Detection, Attention Mechanism, BiLSTM, CNN.


Documentos

Os arquivos do Texto Final (Padrão SBC), o Artigo Publicado e a Apresentação (Slides) estão disponíveis para download na seção de arquivos abaixo.

Anexos

2502.18555v1.pdf Application of Attention Mechanism with Bidirectional Long.pdf Artigo_Aranoua_IFAM_Erick.pdf