Explainable AI for Abnormal Human Activity Detection in Surveillance Videos: A Review

Abstract

This review paper examines the integration of Explainable AI (XAI) techniques into abnormal human activity detection from surveillance videos, emphasizing their significance in enhancing transparency, accountability, and trustworthiness in AI-powered surveillance systems. Through an exploration of XAI methods such as LIME, SHAP, and attention mechanisms, we discuss how these techniques provide insights into the decision-making process of AI models, enabling stakeholders to understand and interpret model predictions. Furthermore, we highlight the advantages of using XAI for debugging errors, identifying biases, and increasing trust among human operators in various security contexts. Additionally, we discuss ongoing research directions in XAI for abnormal human activity detection, including the development of advanced explanation techniques and the incorporation of user feedback into the explanation process. By fostering collaboration between humans and AI, XAI holds the potential to enhance the effectiveness and responsible deployment of surveillance systems, contributing to safer and more secure environments for all stakeholders.