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http://dx.doi.org/10.5302/J.ICROS.2015.14.9055

Fire Detection Algorithm for a Quad-rotor using Ego-motion Compensation  

Lee, Young-Wan (Department of Information & Communication Engineering, Inha University)
Kim, Jin-Hwang (Department of Information & Communication Engineering, Inha University)
Oh, Jeong-Ju (Department of Information & Communication Engineering, Inha University)
Kim, Hakil (Department of Information & Communication Engineering, Inha University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.21, no.1, 2015 , pp. 21-27 More about this Journal
Abstract
A conventional fire detection has been developed based on images captured from a fixed camera. However, It is difficult to apply current algorithms to a flying Quad-rotor to detect fire. To solve this problem, we propose that the fire detection algorithm can be modified for Quad-rotor using Ego-motion compensation. The proposed fire detection algorithm consists of color detection, motion detection, and fire determination using a randomness test. Color detection and randomness test are adapted similarly from an existing algorithm. However, Ego-motion compensation is adapted on motion detection for compensating the degree of Quad-rotor's motion using Planar Projective Transformation based on Optical Flow, RANSAC Algorithm, and Homography. By adapting Ego-motion compensation on the motion detection step, it has been proven that the proposed algorithm has been able to detect fires 83% of the time in hovering mode.
Keywords
quad-rotor; ego-motion compensation; optical flow; RANSAC algorithm; homography; planar projective transformation;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 D. Wang, X. Cui, E. Park, C. Jin, and H. Kim, "Adaptive flame detection using randomness testing and robust features," Fire Safety Journal, vol 55, pp. 116-125, Jan. 2013.   DOI
2 T. Celik, "Fast and efficient method for fire detection using image processing," vol. 32, no. 6, pp. 881-890, Dec. 2010.   DOI
3 B. Jung and G. S. Sukhatme, "Real-time motion tracking from a mobile robot," International Journal of Social Robotics, vol. 2, no. 1, pp. 63-78, Mar. 2010.   DOI
4 R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd Ed, CAMBRIDGE, Cambridge, 2003.
5 J. Shi and C. Tomasi, "Good features to track," Proc. of IEEE Conference on Computer Vision and Pattern Recognition, Seattle, Washington, pp. 593-600, Jun. 1994.
6 B. D. Lucas and T. Kanade, "An iterative image registration technique with an application to stereo vision," Proc. of the 1981 DARPA Imaging Understanding Workshop, pp. 121-130, 1981.
7 G. R. Bradski and A. Kaehler, Learning OpenCV, O'Reilly, 2008.
8 M. Fischler and R. Bolles, "Random sample consensus: A paradigm for model fitting applications to image analysis and automated cartography," Proc. Image Understanding Workshop, pp. 71-88, Apr. 1980.
9 A. Zhao, L. Wang, and C. H. Yao, "Research on electronic-nose application based on wireless sensor networks," International Symposium on Instrumentation Science and Technology, 2006.
10 D. Y. Yun and S. H. Kim, "A design of fire monitoring system based on unmaned helicopter and sensor network," Journal of Korean Institute of Intelligent Systems, vol. 20, no. 4, pp. 516-521, 2010,   DOI
11 K. H. Cheong et al., "Automatic fire detection system using CCD camera and Bayesian network," Image Processing: Machine Vision Applications Book Series: Proceedings of SPIEIS&T Electronic Imaging, vol. 6813, pp. S8130-S8130, 2008.