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2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)
Fire Flame Detection Based on Color Model and Motion Estimation
Dattathreya1, Heegwang Kim2, Jinho Park2, Hasil Park2, and Joonki Paik2*
1
Department of Electronics and Communication
The Oxford College of Engineering, Bangalore - 68, India
2
Image Processing and Intelligent Systems Laboratory
Graduate School of Advanced Imaging Science, and Film
Chung-Ang University, Seoul, Korea
1
E-mail: dattugujjar28@yahoo.com, 2heegwang27@gmail.com, 2dkskzmffps@gmail.com,
2
hahaha2470@cau.ac.kr, 2*paikj@cau.ac.kr
Abstract
Automated fire flame detection in the
surveillance system has drawn significant
attention. This paper presents a novel fire flame
detection algorithm using motion estimation and
color information. The key idea is using irregular
motion vectors of the fire flame. This paper
presents a novel fire flame detection algorithm
using motion estimation and color information.
The proposed algorithm consists of these
following steps; (1) extraction of the fire
candidate region in the HSI color space, (2)
motion estimation of the candidate region using
an optical flow method, (3) quantization of the
motion vectors into 8 directions and (4) detection
of the region where the motion is generated in
various directions as the fire area.
Keywords: fire flame detection, motion
estimation, color model, accumulation image.
1. Introduction
Recently, fire flame detection method using
video analysis is a significant topic in the
Unmanned Aerial System and the intelligent
surveillance system to prevent disasters by fire.
Yu et al. suggested that suspected region of fire
flame could be detected by accumulation image
in HSI color space [1]. Trugay et al. used the
illumination and chromaticity in YCbCr color
space to define the fire flame and they used fuzzy
logic to detect the fire flame [2]. However, these
methods cannot distinguish between the fire
flame and the fire-like colored object. In order to
solve this problem, we propose the method using
color and motion vector of image.
978-1-5090-2743-9/16/$31.00 ©2016 IEEE
2. Fire flame detection algorithm
2.1 Color model
In general, fire flame has the color value of
reddish colors and color saturation of fire
exhibition varying characteristics relating to the
severity of fire in the image. For that reason, the
proposed method extracts the fire candidate
region using HSI color space. The value of H , S
and I for extracting the fire candidate region is
0 d H d 70q
0 d S d 190
210 d I d 260
where H, S and I respectively represent the hue,
saturation and intensity in the HSI color space.
2.2 Motion estimation
In case of detecting fire candidate regions
using only color information, there is false
detection problem that the fire-colored object is
detected as fire. In order to address this problem,
the proposed method additionally uses the motion
vector. In general, an object like human or vehicle
tends to move in regular direction, but the fire
flame has characteristic moving in irregular
direction. Therefore, the false detection can be
decreased by using both the color information and
motion vector. In order to estimate the motion
vector, we use combined local-global approach
with total variation (CLG-TV)
ECLG TV
ª §
³ ««O ¨© ¦P wr
:¬
2
·
º
¹
¼»
u, v ¸ ’u ’v »
where u and v respectively represent
displacements on the x-axis and y-axis directions,
2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)
and P is the image patch. r u, v is the residual
between the previous and the current frames that
is defined as follows.
r u, v
f t f t 1 f xt u f yt v
The estimated motion vectors are quantized
into one of the eight different directions as shown
in Figure 1, and it is detected the final fire flame
that the candidate region showing the quantized
motion vectors that appear in various directions,
not a fixed direction.
Figure 1. Quantization of motion vectors
3. Experimental Results
We used 640x480 size image set for the
experiment, and Figure 2 shows the results of fire
detection. To distinguish the fire region and the
object region, we decide fire region when the
quantized motion vectors generate 4 or more bins
in the histogram.
that the motion vectors generated 4 or more bins
of histogram in the fire region.
4. Conclusion
In this paper, we proposed a novel fire
detection method using HSI color information
and motion vector to detect fire region. First, it
detects the fire candidate region using HSI color
information, then the motion vectors are
quantized into one of the eight different directions.
When the number of quantized motion vectors
exceeds certain number, it is detected to fire
region. In the experiment results, the fire
detection method using motion vector shows
more exact results of fire region detection than
only using color information.
5. Acknowledgements
This work was supported in part by the
Technology Innovation Program (Development
of Smart Video/Audio Surveillance SoC & Core
Component for Onsite Decision Security System)
under Grant 10047788, and by Ministry of
Culture, Sports and Tourism(MCST) and Korea
Creative Content Agency(KOCCA) in the
Culture
Technology(CT)
Research
&
Development Program (R2014040014).
References
(a)
(c )
(b)
(d)
Figure 2. Results of fire flame detection: (a) input
image, (b) detected fire candidate region using
HSI color information, (c) motion vector of
detected fire candidate region, (d) quantized
motion vector histogram of finally detected fire
region
Figure 2(a) shows the input image, and Figure
2(b) represents the detected fire candidate region
using HSI color information. Figure 2(c) shows
the motion vector of detected fire candidate
region of Figure 2(b), and Figure 2(d) represents
the quantized motion vector histogram of finally
detected fire region. The proposed method shows
[1] Y. Chunyu, M. Zhibin, and Z. Xi, “A Realtime Video Fire Flame and Smoke Detection
Algorithm,” 9th Asia-Oceania Symp. Fire Sci.
Technol., vol. 62, pp. 891-898, August 2013.
[2] T. Celik, H. Ozkaramanli, and H. Demirel,
“Fire Pixel Classification using Fuzzy Logic and
Statistical Color Model,” Int. Conf. Acoust.
Speech Signal Process, vol. 1, pp. 1205–1208,
April 2007.
[3] M. Drulea and S. Nedvschi, “Total Variation
Regularization of Local-Global Optical Flow,"
IEEE Int. Conf. Pattern Recognition, pp. 436-439,
August 2010.
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