Object tracking system by integrating multi-sensored data

Kouji Murakami, Tokuo Tsuji, Tsutomu Hasegawa, Ryo Kurazume

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


We propose an object tracking system which recognizes everyday objects and estimates their positions by using distributed sensors in a room and mobile robots. The placement of objects is frequently changed according to human activities. Although a passive RFID tag is attached to each object for the object's recognition, the placement is often not uniquely determined due to the deficiency of measured data. We have already proposed a method for estimating the placement of objects by using the moving trajectories of objects. This estimation result is expressed as the probability distribution of the object placement. However intersections of trajectories cause the decrease of the estimation accuracy. So we propose a new method based on Bayesian inference to improve the estimation accuracy by using the size and the shape of an object measured by laser range finder. Then a mobile robot settles the placement with small workload by using the mounted sensor. The system successfully recognized and localized 10 objects in the experiment.

Original languageEnglish
Title of host publicationProceedings of the IECON 2016 - 42nd Annual Conference of the Industrial Electronics Society
PublisherIEEE Computer Society
Number of pages8
ISBN (Electronic)9781509034741
Publication statusPublished - Dec 21 2016
Event42nd Conference of the Industrial Electronics Society, IECON 2016 - Florence, Italy
Duration: Oct 24 2016Oct 27 2016

Publication series

NameIECON Proceedings (Industrial Electronics Conference)


Other42nd Conference of the Industrial Electronics Society, IECON 2016

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


Dive into the research topics of 'Object tracking system by integrating multi-sensored data'. Together they form a unique fingerprint.

Cite this