An Intelligent Early Warning System for Video Monitoring of Elderly People Living Alone Based on Visual Large Models

The accelerating global trend of population aging has presented an urgent challenge in ensuring the safety and well-being of elderly individuals who live alone. Traditional camera-based monitoring systems often fail to interpret contextual cues, leading to delayed or false alarms. This paper proposes an intelligent early warning system that integrates Visual Large Models (VLMs), multimodal sensing, and explainable AI mechanisms for continuous monitoring of elderly people living independently. The proposed system employs Vision Transformers (ViT) and Contrastive Language-Image Pretraining (CLIP) architectures, enhanced by multimodal fusion of ambient sensors and physiological data, to achieve real-time situational awareness, behavioral anomaly detection, and context-aware alerting. Experimental validation using real-world elderly monitoring datasets demonstrates that the proposed framework outperforms conventional CNN-based models in both accuracy and interpretability. The system achieves a 93.4% alert precision and 91.7% recall, while significantly reducing false alarms through contextual learning. The architecture further ensures privacy preservation via federated learning and differential privacy. This study offers an ethically responsible, scalable, and high-performance solution for intelligent elderly care.