As the global population ages, robust technological solutions are increasingly necessary to support and enhance elderly autonomy in-home or care settings. This paper presents a novel computer vision-based activity monitoring system that uses cameras and infrared sensors to detect and analyze daily activities of elderly individuals in care environments. The system integrates a frame differencing algorithm with adjustable sensitivity parameters and an anomaly detection model tailored to identify deviations from individual behavior patterns without relying on large volumes of labeled data. The system was validated through real-world deployments across multiple care home rooms, demonstrating significant improvements in emergency response times and ensuring resident privacy through anonymized frame differencing views. Upon detecting anomalies in daily routines, the system promptly alerts caregivers and family members, facilitating immediate intervention. The experimental results confirm the system’s capability for unobtrusive, continuous monitoring, laying a strong foundation for scalable remote elderly care services and enhancing the safety and independence of vulnerable older individuals.