Multiple Thermal Sensor Array Fusion Toward Enabling Privacy-Preserving Human Monitoring Applications

Human-centric applications of a single thermal sensor array (TSA) have performed extremely well in many areas. However, most of these works have not yet reached the real applicability stage of the Internet of Things (IoT) applications. The main limitation of deploying such systems on a large scale is the challenge of fusing multiple TSAs to cover a wide inspection area, e.g., smart homes, hospitals, and many other domestic environments. On the other hand, objects that appear in the low-resolution thermal images acquired from TSA have low intraclass variations and high interclass similarities, making the identification of the overlapping regions through matching a comparable template image in multiple images very difficult. This article proposes a motion-based approach to fuse multiple TSAs and learn the domestic environment layout to enable further human-centred IoT applications to run in the cloud. Besides, a privacy improvement on utilizing these sensors in IoT applications is proposed. The proposed approach is evaluated with comprehensive experiments on different sensor placements and domestic environment conditions. This article shows an average performance of 92.5% accuracy using various machine learning techniques and use case scenarios.