Marker-Free Indoor Localization and Tracking of Multiple Users in Smart Environments Using a Camera-Based Approach
In recent years, various indoor tracking and localization approaches for usage in conjunction with Pervasive Computing systems have been proposed. In a nutshell, three categories of localization methods can be identified, namely active marker-based solutions, passive marker-based solutions, and marker-free solutions. Both active and passive marker-based solutions require a person to carry some type of tagging item in order to function, which, for a multitude of reasons, makes them less favorable than marker-free solutions, which are capable of localizing persons without additional accessories. In this work, we present a marker-free, camera-based approach for use in typical indoor environments that has been designed for reliability and cost-effectiveness. We were able to successfully evaluate the system with two persons and initial tests promise the potential to increase the number of users that can be simultaneously tracked even further.
KeywordsIndoor localization Computer Vision Pervasive Computing
Unable to display preview. Download preview PDF.
- 1.Chessa, S., Knauth, S.: Evaluating AAL Systems Through Competitive Benchmarking. Indoor Localization and Tracking. Springer, Heidelberg (2012)Google Scholar
- 3.Sturim, D.E., Brandstein, M.S., Silverman, H.F.: Tracking multiple talkers using microphone-array measurements. IEEE Comput. Soc. Press (1997)Google Scholar
- 4.Krumm, J., Harris, S., Meyers, B., Brumitt, B., Hale, M., Shafer, S.: Multi-camera multi-person tracking for EasyLiving. In: Proceedings Third IEEE International Workshop on Visual Surveillance, pp. 3–10. IEEE Comput. Soc. (2000)Google Scholar
- 5.Lauterbach, C., Steinhage, A.: SensFloor ® - A Large-area Sensor System Based on Printed Textiles Printed Electronics. In: Ambient Assisted Living Congress. VDE Verlag (2009)Google Scholar
- 7.Guo, Y., Hazas, M.: Localising speech, footsteps and other sounds using resource-constrained devices. In: 10th International Conference on Information Processing in Sensor Networks (IPSN), pp. 330–341 (2011)Google Scholar
- 8.Balakrishnan, H.: The Cricket Indoor Location System. Doctoral Dissertation, Massachusetts Institute of Technology (2005) Google Scholar
- 11.Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: CVPR 2011, pp. 1297–1304 (2011)Google Scholar