Augmenting Anomaly Detection with Tiny Cameras
Project Overview
Investigates how low-cost cameras can complement sensor networks to detect anomalies in smart environments.
Smart home environments rely on sensor networks to detect unusual patterns and behaviours. However, traditional sensors often miss contextual cues that a visual observation would reveal. This project explores how miniature, low-cost cameras can augment existing anomaly detection systems, adding a layer of visual intelligence without compromising privacy or affordability.
The research builds prototypes that combine simple image capture with edge analytics to enrich anomaly detection pipelines. By processing images locally on resource-constrained devices, the approach minimises data transfer and preserves occupant privacy. The project studies trade-offs between camera placement, lighting conditions, and the fusion of visual signals with other sensor data to improve detection accuracy in residential IoT settings.
Team
