Distributed Multi-Human Location Algorithm Using Naive Bayes Classifier for a Binary Pyroelectric Infrared Sensor Tracking System

This paper presents a distributed multi-human location algorithm for a binary pyroelectric infrared sensor tracking system. The tracking space of our system is divided into many uniform static sub-regions. A two-level regional location, static partitioning and dynamic partitioning, is proposed. A Naive Bayes classifier is used to simplify the human location in a static sub-region, and we achieve the initial location of human by fusing all the internal measuring points of infrared sensors. Taking this initial location as the center, a new secondary dynamic sub-region is defined and all its internal measuring points of infrared sensors are fused again to get the ultimate human location. The simulation and experimental results demonstrate that the proposed method has improved the locating accuracy of multiple human targets with low computational cost in infrared sensor tracking system.