Two-Stage Violence Detection Using ViTPose and Classification Models at
  Smart Airports
İrem Üstek Jay Desai Iván López Torrecillas Sofiane Abadou Jinjie Wang Quentin Fever Sandhya Rani Kasthuri Yang Xing Weisi Guo Antonios Tsourdos
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. It 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 SAFE (Situational Awareness for Enhanced Security framework of SAAB, 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, implementing 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.