Two-stage violence detection using ViTPose and classification models at smart airports

Abstract

This study introduces an innovative violence detection framework tailored to the unique requirements of smart airports, where prompt responses to violent situations are crucial. The proposed framework harnesses the power of ViTPose for human pose estimation and employs a CNN-BiLSTM network to analyse spatial and temporal information within keypoints sequences, enabling the accurate classification of violent behaviour in real-time. Seamlessly integrated within the SAAB’s SAFE (Situational Awareness for Enhanced Security) framework, the solution underwent integrated testing to ensure robust performance in real-world scenarios. The AIRTLab dataset, characterized by its high video quality and relevance to surveillance scenarios, is utilized in this study to enhance the model's accuracy and mitigate false positives. As airports face increased foot traffic in the post-pandemic era, the implementation of AI-driven violence detection systems, such as the one proposed, is paramount for improving security, expediting response times, and promoting data-informed decision-making. The implementation of this framework not only diminishes the probability of violent events but also assists surveillance teams in effectively addressing potential threats, ultimately fostering a more secure and protected aviation sector. Codes are available at: https://github.com/Asami-1/GDP.