## 2 Related Works
### Subject Anonymization  
In elderly care, privacy-preserving monitoring systems often utilize ambient sensors or wearables to provide passive monitoring, thereby addressing the privacy concerns posed by camera surveillance [42]. These technologies capture essential data regarding the physical well-being and activity levels of elderly people without the invasiveness [45; 46; 47; 48; 49] associated with video recordings. However, while sensors and wearables offer a less intrusive means of observation, they may not always provide the comprehensive visual context necessary for certain caregiving tasks. Furthermore, several vision-based monitoring systems [10; 26; 33; 50; 51] have proven to be more capable of detecting emergency situations, such as falls, making them more popular among caregivers and less inconvenient for elderly people than wearables.  
Subject anonymization is a technique widely used in computer vision for enhancing visual privacy and making it harder to identify subjects under surveillance. Early works in this field focused on methods such as face identification and warping [52] to render the faces of subjects unidentifiable. Some common anonymization methods [53] in video surveillance, including blanking, obfuscation, abstraction, and encryption, safeguard personal identities by first detecting individuals in video feeds and then applying privacy-enhancing techniques to these areas. Blanking replaces sensitive regions with background imagery, obfuscation (via pixelation, blurring, or warping) and abstraction (employing edge detection, avatars, or silhouettes) modify visual details to prevent recognition, and encryption ensures data privacy by restricting access to selected areas with decryption keys, offering strong privacy safeguards. Recent innovations in visual privacy techniques [54; 55] focus on whole-body or face detection/segmentation, altering visual representations through methods like blurring, pixelation [56], and embossing; replacing them with silhouettes, skeleton representations, and three-dimensional avatars; or complete invisibility, further advancing privacy protection. Yet the literature indicates that many of these methods might necessitate manual image manipulation or entail substantial processing time, limiting their applicability in real-time systems.  
Other privacy techniques specifically designed for elderly monitoring systems include the use of depth images, rather than solely RGB images, to develop fall detection systems [40; 57]. Depth images, by only revealing spatial information, enable accurate fall detection without compromising visual privacy. Infrared/thermal camera-based monitoring systems [11; 12; 58; 59], on the other hand, capture heat signatures, further reducing visible details and enhancing privacy for elderly people. While these methods offer effective monitoring while prioritizing individual privacy, they also restrict the visual information often needed for contextual understanding in caregiving.  
Given the outlined challenges and advancements in privacy-preserving technologies for elderly monitoring, there is a pressing need for automated systems capable of providing efficient subject anonymization while ensuring effective surveillance. The system proposed in this paper uses the latest advancements in computer vision and AI to automatically protect individuals' identities while still keeping a close eye on their well-being. It is designed to dynamically adapt to the complexities of real-world scenarios, ensuring that privacy does not compromise the capacity to identify and react to emergencies. With a core focus on the fundamental objectives of surveillance--safety and well-being--the proposed system introduces a privacy-aware approach that retains the integrity of elderly monitoring. This innovative solution effectively meets the dual demands of preserving privacy and delivering comprehensive care, creating an ethical surveillance system through the application of state-of-the-art technology.