Abstract

Privacy-preserving human action recognition is a crucial area of research, particularly in the context of video surveillance, assisted living systems, and healthcare applications. While human action recognition techniques offer significant benefits for automated video analysis, they also raise concerns about individual privacy when deployed in sensitive environments. This paper introduces, StealthGuard incorporates a temporal privacy-preserving component based on generative adversarial networks (GANs) to obfuscate sensor data, thereby preventing the identification of individual people or their activities. This approach utilises deep neural network, ensuring both accuracy in action recognition and real-time deployment feasibility. Through extensive experimental results, StealthGuard demonstrates its ability to achieve high levels of privacy protection while maintaining recognition accuracy making it a promising solution for applications where privacy is paramount. This paper also provides a related works in the field, highlighting approaches and techniques for privacy-preserving human action recognition.