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

TL;DR

This paper presents a deep learning approach combining Attention Mechanism, CNN, and BiLSTM to improve automatic detection of human conflicts in videos, addressing challenges like data scarcity and complex interactions.

Contribution

It introduces an integrated model leveraging attention, CNN, and BiLSTM for enhanced conflict detection in videos, advancing automated surveillance capabilities.

Findings

Attention mechanism improves focus on relevant video segments.

Combined CNN, BiLSTM, and attention yield higher detection accuracy.

Model shows potential for real-time conflict monitoring.

Abstract

The automatic detection of human conflicts through videos is a crucial area in computer vision, with significant applications in monitoring and public safety policies. However, the scarcity of public datasets and the complexity of human interactions make this task challenging. This study investigates the integration of advanced deep learning techniques, including Attention Mechanism, Convolutional Neural Networks (CNNs), and Bidirectional Long ShortTerm 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. This work opens new possibilities for the development of automated surveillance systems that can operate more efficiently in real-time detection of violent events.