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.
