Object detection under the lens of privacy: A critical survey of methods, challenges, and future directions

Abstract

This paper presents critical surveillance system functions and considers advances and challenges for privacy and ethical implications. We examine privacy protection strategies and responsible data management practices reflecting the critical problems of cybersecurity and data management to address concerns, ensuring that surveillance technology evolves ethically and sustainably. We highlight state-of-the-art deep learning-based techniques in object detection, addressing the complex difficulties of surveillance systems. Finally, we provide a research framework, including ethical and long-term implications and broad applications in privacy preservation and ethical responsibility. This study advances knowledge to ensure the long-term sustainability, security, and viability of information and communication technology applications.