Multiperson Locating and Their Soft Tracking in a Binary Infrared Sensor Network

Autor: Jun Toyama, Bing-Nan Pei, Mineichi Kudo, Hidetoshi Nonaka, Shuai Tao
Rok vydání: 2015
Předmět:
Zdroj: IEEE Transactions on Human-Machine Systems. 45:550-561
ISSN: 2168-2305
2168-2291
DOI: 10.1109/thms.2014.2365466
Popis: Low-cost sensor networks for multitarget tracking are increasingly becoming important equipment in many applications. A major problem is that these sensors usually provide only a binary response in each epoch, if a target is present or absent. Efficient approaches for realizing the location and tracking of multiple targets are needed. In this paper, we develop a soft tracking system using an infrared ceiling sensor network and propose a novel algorithm for tracking multiple people. In this system, 43 infrared sensors were attached to the ceiling of an office room (15.0 m $\times$ 8.5 m). Some pieces of weak evidence such as locations of personal desks, and the moving directions of people, were used for soft tracking. Through experiments, the ability of tracking in different situations was evaluated. The tracking accuracy during a 3 h period was investigated. The results showed that this system was able to track up to eight people simultaneously for hours in an office room. The tracking accuracy was above 90% most of the time, although some identity ambiguities occurred.
Databáze: OpenAIRE