Uncertainty Boosted Activity Anticipation in Elderly Apartment Surveillance with predictive elderly care alerts
Keywords:
Activity Anticipation, Elderly Care, Surveillance Systems, Uncertainty Estimation, Predictive ModelingAbstract
The rapid global increase in the aging population has necessitated the development of intelligent, non-intrusive surveillance systems to support independent living in private apartments. Traditional video analytics predominantly focus on action recognition, which identifies events only after they have occurred, offering limited utility for proactive intervention. Activity anticipation, the prediction of future actions based on partially observed video segments, presents a proactive alternative but is severely hindered by the high intra-class variance and inherent unpredictability of elderly movements. This paper proposes a novel conceptual framework termed Uncertainty-Boosted Activity Anticipation, which leverages predictive uncertainty not as a detrimental artifact to be minimized, but as a highly informative feature to enhance long-term anticipation accuracy. By decoupling aleatoric uncertainty inherent in the visual data and epistemic uncertainty present in the model architecture, the proposed approach dynamically allocates attention to spatiotemporal regions with high predictive variance. Through rigorous qualitative and quantitative analysis over simulated surveillance datasets, the conceptual model demonstrates superior anticipation capabilities, particularly in critical transitional actions such as transitioning from standing to sitting or sudden postural instability. The findings provide a comprehensive architectural foundation for next-generation elderly-care surveillance, balancing safety through proactive monitoring with the complexities of unstructured apartment environments.
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