DocumentCode :
1922215
Title :
Robust Little Flame Detection on Real-Time Video Surveillance System
Author :
Lai, Tai Yu ; Kuo, Jong Yih ; FanJiang, Yong-Yi ; Ma, Shang-Pin ; Liao, Yi Han
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Taipei Univ. of Technol., Taipei, Taiwan
fYear :
2012
fDate :
26-28 Sept. 2012
Firstpage :
139
Lastpage :
143
Abstract :
This study proposed a method to detect the little flame in the early stage of fire combustion. The foreground object was extracted by motion detection and YCb Cr color clues. To avoid the noise of motion detection in different resolution videos, background edge model is used to eliminate noise instead of morphology. Next, with the help of fire characteristics, the foreground object is identified. A fire object is determined by compactness, corner flicker rate, and growth rate. The experiment can be applied to any resolution video and complex scene, both indoors and outdoors, such as squares, where people walk around and vehicles pass by. The outcome of experiment, using this proposed method, can detect the fire object accurately and exclude the undangerous fire.
Keywords :
feature extraction; fires; flames; image denoising; image resolution; object detection; video surveillance; YCb Cr color clues; background edge model; complex scene; corner flicker rate; fire characteristics; fire combustion; fire object detection; foreground object extraction; growth rate; morphology; motion detection noise elimination; real-time video surveillance system; resolution videos; robust little-flame detection; Conferences; Feature extraction; Fires; Image color analysis; Image edge detection; Motion detection; Noise; Flame Detection; Video Surveillance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Innovations in Bio-Inspired Computing and Applications (IBICA), 2012 Third International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-1-4673-2838-8
Type :
conf
DOI :
10.1109/IBICA.2012.41
Filename :
6337652
Link To Document :
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