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

• Published in: IEEE sensors journal Vol. 16; no. 1; pp. 216 - 223
• Main Authors: Yang, Bo, Lei, Yiqun, Yan, Bei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Binary systems (materials); Classification algorithms; Dynamics; Human; Human target location; human target tracking; Infrared; Infrared sensors; Naive Bayes classifier; Neural networks; Partitioning; Partitioning algorithms; pyroelectric infrared sensor network; region partition; Sensors; Target tracking; Tracking systems; pyroelectric infrared sensor network; human target tracking; region partition; Naive Bayes classifier; Human target location
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2015.2477540

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.