ACM Home Page
Please provide us with feedback. Feedback
Intra-frame scan: a method for estimating object movements using an interlace camera and visual markers
Full text PdfPdf (1.05 MB)
Source International Conference on Mobile Computing and Multimedia archive
Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia table of contents
Linz, Austria
SESSION: MoMM 2008: Computer vision table of contents
Pages 125-129  
Year of Publication: 2008
ISBN:978-1-60558-269-6
Authors
Yuhki Suzuki  Kobe University, Kobe, Japan
Tsutomu Terada  Kobe University, Kobe, Japan
Masahiko Tsukamoto  Kobe University, Kobe, Japan
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 2,   Downloads (12 Months): 32,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1497185.1497213
What is a DOI?

ABSTRACT

There has recently been a great deal of research on location-dependent services, such as navigation systems where these estimate the location of participants in event spaces and provide navigation for them. There are methods of estimating absolute positions such as those using GPS and those using RFID tags. However, they have problems with the high installation cost and environmental restrictions. Therefore, relative-position estimation is a strong candidate that is inexpensive and can be broadly adapted. Although there are existing methods of estimating relative positions using wearable sensors, they have problems with the cost of managing multiple sensors. There is a method using an optical flow of camera images. However, since it needs a high degree of computational power for calculating optical flows, it cannot respond to high-speed motion. In this paper, we propose a new method of estimating relative positions using simple markers and an interlace camera. Since our system estimates the relative distance by using two successive images with a very short scanning interval captured by an interlace camera, it can adapt to high-speed movement.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
M. Kourogi, N. Sakata, T. Okuma, and T. Kurata: Indoor/Outdoor Pedestrian Navigation with an Embedded GPS/RFID/Self-contained Sensor System, Proc. of 16th International Conference on Artificial Reality and Telexistence (ICAT2006), pp. 1310--1321, 2006.
 
2
 
3
D. Hahnel, W. Burgard, D. Fox, K. Fishkin, and M. Philipose: Mapping and Localization with RFID Technology, Proc. of IEEE International Conference on Robotics and Automation, vol. 1, pp. 1015--1020, 2004.
 
4
L. Fang, W. Du, and P. Ning: A Beacon-Less Location Discovery Scheme for Wireless Sensor Networks, Proc. of 24th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 1, pp. 161--171, 2005.
 
5
M. Kalkusch, T. Lidy, M. Knapp, G. Reitmayr, H. Kaufmann, and D. Schmalstieg: Structured Visual Markers for Indoors Pathfinding, Proc. of The First IEEE International Augmented Reality Toolkit Workshop(ART02), 2002.
 
6
 
7
C. Braillon, C. Pradalier, J. L. Crowley, and C. Laugier: Real-time moving obstacle detection using optical flow models, Proc. of IEEE Intelligent Vehicles Symposium 2006, pp. 466--471, 2006.

Collaborative Colleagues:
Yuhki Suzuki: colleagues
Tsutomu Terada: colleagues
Masahiko Tsukamoto: colleagues