Flame detection algorithm based on a saliency

Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space Zhao-Guang Liu, Yang Yang & Xiu-Hua Ji

Signal, Image and Video Processing ISSN 1863-1703 Volume 10 Number 2 SIViP (2016) 10:277-284 DOI 10.1007/s11760-014-0738-0

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Author's personal copy SIViP (2016) 10:277–284 DOI 10.1007/s11760-014-0738-0

ORIGINAL PAPER

Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space Zhao-Guang Liu · Yang Yang · Xiu-Hua Ji

Received: 26 February 2014 / Revised: 8 December 2014 / Accepted: 11 December 2014 / Published online: 13 January 2015 © Springer-Verlag London 2015

Abstract Computer vision-based fire detection involves flame detection and smoke detection. This paper proposes a new flame detection algorithm that is based on a saliency detection technique and on the uniform local binary pattern (ULBP). In still images and video sequences, an area that contains an open flame is always noticeable because fire is an exceptional event. Thus, to utilize the color information of flame pixels, the probability density function (pdf) of the flame pixel color can be obtained using Parzen window nonparametric estimation. This a priori pdf is then fused with the saliency detection phase as top-down information so that the flame candidate area can be extracted. To reduce the number of false alarms, the image texture of the candidate area is analyzed by ULBP, and an exponential function with two parameters is utilized to model the texture of the flame area. According to the experimental results, our proposed method can reduce the number of false alarms greatly compared with an alternative algorithm, while ensuring the accurate classification of positive samples. The classification performance of our proposed method is proven to be better than that of alternative algorithms. Keywords Flame detection · Saliency detection · Uniform local binary pattern · YCbCr

1 Introduction The performance of traditional fire detection systems based on particles and temperature depends on the position and quantity of sensors. Therefore, the effectiveness of these systems is limited in large areas, e.g., forest, open land, and stadiums. This is one of the major limitations of traditional fire detectors. Computer vision-based fire detection has become an extremely popular topic during the last decade, and such detection comprises fire detection [1] and smoke detection. The present study addresses the former aspect. In computer vision-based flame detection techniques, the most frequently used features are color [1], texture [2], shape [3], and motion [3,4]. According to many previous studies, fire has distinct color characteristics, i.e., the color of fire is in the red-yellow range [3]. The color information of a pixel is generally used to determine whether a pixel belongs to a candidate flame area; moreover, the most commonly used color spaces are RGB [5–7], YUV [8,9], CIEL*a*b* [10], HSV [11], and YCbCr [12]. With respect to color characteristics, the methods used in previous studies can be classified into the three categories described in the following subsections. 1.1 Methods based on distribution

Z.-G. Liu (B) · X.-H. Ji Shandong Provincial Key Laboratory of Digital Media Technology, School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China e-mail: [email protected] Y. Yang School of Information Science and Engineering, Shandong University, Jinan, China e-mail: [email protected]

For the methods based on distribution, the flame pixel color is assumed to be concentrated in specific zones, and probability density functions (pdfs) or other equations are used to match these distributions. In a previous study [5], the RGB channel distribution of each flame pixel color was assumed to be independent, and a unimodal Gaussian model was used to describe this distribution. If the overall probability distribution of a pixel color in a frame was greater than a threshold, the pixel was tagged as a flame pixel. Based on the obser-

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vations of the possible color values of a three-dimensional cloud in the RGB color space, another study [13] estimated a Gaussian mixture model using ten Gaussian distributions. If a given pixel color value was inside one of the distributions, it was assumed to be a flame-colored pixel. In some studies, rules were established to describe the distribution of the flame pixel color, which was assumed to be specific zones. If a given pixel color fell within a zone, it was considered to be a flame pixel. In a previous study [14], the area that contained flame pixels in the Cb-Cr plane was modeled based on the intersection of three polynomials and five other equations. 1.2 Methods based on features For the methods based on features, the pixel color is selected as a pixel feature. In a previous study [15], the pixel color values and the first and second derivatives of the pixel color intensity were used to generate a covariance matrix. The elements in the covariance matrix were extracted as features and entered as inputs into a support vector machine (SVM) classifier. Previously [16], the CIE L*a*b* color space was also used to construct a generic chrominance model for flame pixel segmentation. The chrominance components A and B were then extracted as features to be used by the fuzzy cmeans algorithm. 1.3 Methods based on thresholds For the methods based on thresholds, certain thresholds or rules are established that determine the flame pixel color. Chen et al. [17] established the following rules for determining the flame pixel color: R > RT , R > G > B, and S ≥ (255 − R) · ST /RT , where ST is the saturation value when the value of the R channel is RT . If these three conditions are satisfied by a pixel, the pixel is considered to be fire-colored. In texture analyses, the statistical mean [1,3,16], variance [1,3,16], skewness [1,3], and kurtosis [1] are often used to characterize flame pixels. These statistics are extracted as features to be used by classifiers, e.g., SVMs [16], neural networks [1], and Bayesian classifiers [3]. The focus of the present study is flame detection. A flame is an exceptional event in an image or a video, and it is always noticeable. Saliency detection techniques were developed to extract the most notable areas in images and videos, and they can be utilized to extract areas that contain flames. Inspired by this idea, we propose a new flame detection algorithm based on saliency detection fused with the color information of a flame pixel. Furthermore, the uniform local binary pattern (ULBP) is used to analyze the texture of the flame candidate area, and an exponent function with two parameters is used to model the ULBP of the flame area.

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The remainder of this paper is organized as follows. Section 2 provides background information. Section 3 proposes the flame detection algorithm based on saliency detection and ULBP. Section 4 presents our experimental results based on still images and video sequences. Finally, Sect. 5 states our conclusions. 2 Background information 2.1 Saliency detection Saliency detection is an extremely popular research topic in computer vision, and it has been applied in many fields, e.g., object segmentation [18], object tracking [19], and robot vision [20]. The basic idea of saliency detection is as follows. First, various features are extracted from the input image, e.g., intensity [21], color [21], and texture [22], and sub-saliency maps are created using these features. The final result is extracted after combining the sub-saliency maps, e.g., using weighting [22] and normalization [21]. A saliency map is a grayscale image where higher values represent areas that are more noticeable. 2.2 Local binary pattern (LBP) An LBP is a local texture descriptor used in image processing, and it has been applied successfully to face recognition [23], object detection [24], and texture classification [25]. For a grayscale image I , (xc , yc ) are the pixel coordinates and Ic = I (xc , yc ) is its intensity value. In = I (xn , yn ), n = 0, 1, P − 1 are the spatial neighbors of (xc , yc ), where P is the number of neighbors. The coordinates of xn , yn can be calculated as follows:   2π n xn = xc + R cos P   2π n (1) yn = yc − R sin P where R is the radius. An extension of the original operator is the ULBP, which uses a uniformity measure of a pattern, i.e., U (pattern) is the number of bitwise transitions from zero to one, or vice versa, when the bit pattern is considered circular. The LBP is considered uniform if its uniformity measure is two at most. There are two reasons for omitting non-uniform patterns [25]. First, most of the local binary patterns in natural images are uniform. The second reason for considering uniform patterns is their statistical robustness. Using uniform patterns instead of all the possible patterns has produced better recognition results in many applications.

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3 Flame detection algorithm based on saliency detection and ULBP The overall scheme of the proposed method is shown in Fig. 1. The content in the rectangle is the processing component, and the content in the ellipse is the input image or the processing result. A saliency map (S M_map) is created from the input image through saliency detection. The color information is extracted from the input image based on the prior probability density distribution, and a similar map called the P D F_map is created. S M_map and P D F_map are both gray images. A higher value in the S M_map attracts more attention. A flame region in an image or video is always an exceptional event and it has a center-surround [21] characteristic during saliency detection. The P D F_map is created using color information, and its value represents the probability of classification as a flame pixel. The two maps are fused, and the final map (M A P) is generated. After image binarization and morphological processing using M A P, a mask is created that represents the flame candidate region. In the final phase, a texture analysis based on the ULBP is used to remove interfering objects, e.g., red cars and flowers. 3.1 Saliency detection In real applications, the top-down information is added to extract the desired object. The focus of the current study is flame detection. In most cases, flames are exceptional events in images or videos, and saliency detection techniques can extract flame regions from images with certainty. However, saliency detection can extract other notable objects in addition to flame regions. Thus, other information should be included in order to eliminate the extraction of interfering objects during saliency detection. Color information plays an important role [2,14,26] in flame detection, and it is utilized in the present method. We collected 2,000 images from the Internet, and cropped images from fire videos in different scenarios, including day-

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time, nighttime, forest, vehicle, plane, building, and bonfire scenes. The flame regions were identified manually in the collected images. In the 2,000 images, a total of 26,626,753 (25.39 M) pixels were marked as flame pixels. The flame pixel pdfs in the YCbCr color space were obtained using a nonparametric estimation technique, i.e., the Parzen window approach [26]. Each pixel (x, y) in an image is processed as follows: P D F_map(x, y) = P D F(V1 (x, y), V2 (x, y), V3 (x, y)) (2) where V1 , V2 , and V3 are the values of (x, y) in the three channels of the YCbCr color space, i.e., Y, Cb, and Cr. P D F is the prior pdf of a flame pixel according to the Parzen window nonparametric estimation. Similar to the saliency map (S M_map in Fig. 1), the P D F_map is a gray image, and the value calculated using Eq. 2 represents the probability of its classification as a flame pixel. In the present method, P D F_map and S M_map are normalized as follows: map(x, y) =

map(x, y) − min(map) max(map) − min(map)

(3)

where the operators “min” and “max” find the minimum and maximum values in the S M_map or P D F_map, respectively. Therefore, the color feature of a flame pixel is fused into the saliency map as prior information, and M A P is calculated as follows, M A P(x, y) = S M_map(x, y) · P D F_map(x, y)

(4)

An example of saliency detection fused with color information is shown in Fig. 2. The bottom-up saliency detection result (P D F_map) is shown in Fig. 2b. The white house in the top right of the original image was extracted through saliency detection because it was an exceptional area of the image. However, this house was removed based on the color information, as shown in Fig. 2c. Therefore, the final saliency detection result (M A P) fused with the color information does not include this interfering object. 3.2 Threshold determination during image binarization M A P is generated by fusing the saliency detection result and the color information of the flame pixel. Image binarization is necessary to extract the candidate flame regions, and the resulting mask is calculated as follows  1, if M A P(x, y) > th M AS K (x, y) = (5) 0, otherwise

Fig. 1 Overall scheme of the proposed flame detection algorithm

where th is the threshold used for image binarization. To determine the threshold of the image binarization, a th-A P

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Fig. 2 Saliency detection results. a Image, b P D Fmap , c S Mmap , d MAP

(b)

(a)

(c)

(d)

3.3 Texture analysis based on ULBP The objects extracted by saliency detection fused with color information might not always be flame pixels. For example, red flowers or yellow cars can be noticeable also, and they have a similar color distribution to flame pixels. Texture analysis is necessary to remove these interfering objects; furthermore, texture is a commonly used feature in flame detection [1–3,16]. As a local descriptor, ULBP has been used in many applications. Figure 4a–c shows the ULBP results for the flame areas that were marked manually in the YCbCr color space. To describe ULBP using a simple model, the histogram is re-sorted from the minimum value to the maximum value. Let h(i), i = 0, 1, , 2 P − 1 be the original histogram function. The sorting process can be viewed as a readjustment of the index i. If we let s(i), i = 0, 1, , 2 P −1 be the re-sorted index function, the re-sorted histogram function is as follows:

Fig. 3 Threshold determination during image binarization

curve is calculated. A P is calculated as follows W −1  H −1 x=0 y=0 M AS K (x, y) Ap = Af

(6)

where A f is the manually marked area of the flame pixels, whose value is known. The M AS K image size is W × H . The th-A P curve is calculated using the following pseudo-code: (a) th(i) = i/(Bin − 1), i = 0, 1, , Bin − 1, where Bin is the quantization step of th; in this study, Bin = 100; (b) k = 0; (c) A P (k) = 0, n = 0; (d) Saliency detection and fusion with the P D F_map for the nth image I (n), which generates M A P; (e) Calculate M AS K for the nth image using Eq. 5 with th(k); (f) Calculate A P (n) for the nth image using Eq. 6, where A P (k) = A P (k) + A P (n); (g) n = n + 1, but if n < N (N is the number of images), go to step (d); (h) A P (k) = A P (k)/N , k = k + 1, but if k < Bin, go to step (c). Otherwise, END. In this study, the images used to calculate the th − A P curve are the same 2,000 images employed to estimate the pdf, i.e., N = 2, 000. The results are shown in Fig. 3. The horizontal and vertical ordinates are th and A p , respectively. The threshold th was selected when A P = 80 %.

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h  (i) = h(s(i)), i = 0, 1, . . . , 2 P − 1

(7)

For example, when P = 2 and the original histogram function is h(i) = 5, 9, 6, 4, the index function s(i) = 3, 0, 2, 1 can be obtained after re-sorting the histogram. Therefore, the re-sorted histogram function can be described as h  (i) = h(s(i)) = 4, 5, 6, 9, i = 0, 1, 2, 3. The re-sorted results h  (i) are shown in Fig. 4d–f. Clearly, an exponent function can be used to fit the re-sorted histogram h  (i). The model in Eq. 8 is used in the proposed method. ˆ = λ1 eλ2 i , i = 0, 1, . . . , 2 P − 1 h(i)

(8)

After processing with the “ln” function, the following linear regression model is obtained. ˆ = lnλ1 + λ2 i, i = 0, 1, . . . , 2 P − 1 ln h(i)

(9)

It is well known that least-squares estimation can be used to estimate the parameters for linear regression models. The fitted results for the Y, Cb, and Cr channels are shown as red lines in Fig. 4d–f, which can model the actual re-sorted histograms of the three channels. The results in Fig. 4 are the statistical results obtained from 2,000 images. For one flame area in a single image, the ULBP histogram h1(i), i = 0, 1, , 2 P − 1 is re-sorted using Eq. 7, and the result is as follows: h 1 (i) = h 1 (s(i)), i = 0, 1, . . . , 2 P − 1

(10)

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Fig. 4 ULBP results for flame pixels and fitted results. a Y channel, b Cb channel, c Cr channel, d Y channel, e Cb channel, f Cr channel

3.4 Determining parameters λ1 and λ2

change in the area size. The normalized area change for the ith frame is given by Borges and Izquierdo [3]:

To determine the parameters in the exponential model, the ULBP histograms of the flame areas in the 2,000 images were extracted and fitted to exponential models separately. Thus, 2,000 λ1 values were obtained. The distributions of these λ1 values in the YCbCr color space are shown in Fig. 5. According to the experimental results, with the exception of λ1 , λ2 in the Y channel, the parameters in the other two channels have good Gaussian distributions. For a normally distributed random variable, a distance of two times the standard deviation covers 95 % of the points, and a distance of three times the standard deviation covers 99 % of the points [26]. In the proposed method, the criterion used to classify an area as flame during texture analysis is as follows: μ − 2σ ≤ λ ≤ μ + 2σ

(11)

where μ and σ are the means and standard deviations of λ1 , λ2 for the Cb and Cr channels, respectively, extracted from 2,000 images, which can be obtained by the maximum likelihood estimation. 3.5 The utilization of other information 3.5.1 Randomness of area size For the extracted candidate flame region, because of the flame flickering, a change in the area size of the flame region occurs from frame to frame. Non-flame areas have a less random

3.5.2 Boundary roughness The shape of flame regions can be characterized by Fourier descriptors (FD) , but it is time-consuming thus not suitable for real-time flame detection applications. The convex hull of a set of pixels S is the smallest convex set containing S. In this study, the boundary roughness of the potential flame region is given by Borges and Izquierdo [3]. 4 Experimental results The proposed algorithm is based on still images, but it can be applied to video sequences. Therefore, we performed experiments using still images and videos. In the experiments with still images, 1,000 samples were collected from the Internet, and they are comprised of 600 positive samples and 400 negative samples. Interfering objects are included in the negative samples, e.g., a red flower, a red flag, and the sun. Some examples are shown in figure. Six video clips were collected from the Internet. The description of these videos is provided in Table 1, and sample frames of the test videos are shown in Fig. 6. The scenes in the videos include daylight, forest, night, buildings, and vehicles. The experiments were implemented on a Core i5 computer running at 3.20 GHz with 4 GB of memory using the C++ programming language. Three other methods [9,14],

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Fig. 5 Distributions of λ1 and λ2 parameters in YCbCr color space. a λ1 of Y channel, b λ1 of Cb channel, c λ1 of Cr channel

Table 1 Video clip description Name

Frame size

Number of frames

Number of fire frames

Description

Movie1 320 × 240

155

0

Movie2 400 × 256

246

246

Daylight, forest fire

Movie3 320 × 240

468

468

Nightfall, camera with movement

Movie4 440 × 328 1,898

1,110

Daylight, camera with movement

Movie5 440 × 328 1,544

1,080

Daylight, camera with movement

Movie6 448 × 336

595

491

Negative video, night

Night, camera with movement

and [15], proposed to detect flame in a video and denoted as Refs. [9,14,15] were also implemented in the experiments; moreover, the results using these methods were compared with those obtained using our proposed method. The classification accuracy comparison is listed in Tables 2 and 3. In addition, a computational efficiency comparison was implemented in the experiments; the results are listed in Table 4. Ref. [14] is also based on still images, and it is extremely similar to our proposed method. The experimental results obtained using this method includes both still images and videos; moreover, the results of this method were compared with the results of our proposed method. The criteria used to compare the classification accuracy are the true positive rate, true negative rate, and accuracy [27]. NaN indicates where the denominator and the numerator in the calculation are both zero. Movie1 is a video with interference that contains no fire frames. Therefore, the TPRate values for the video is NaN. However, Movie2 and Movie3 both contain fire frames and the TNRate values for these videos are all NaN. Based on our definition, TPRate describes the proportion of positive samples that are classified correctly. A higher TPRate value indicates a better classifier performance for positive samples. TNRate represents the proportion of negative samples that are classified correctly. A higher

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TNRate value represents a lower false positive rate. Accuracy describes the classification accuracy for all samples, which has been used in many previous studies. According to the experimental results listed in Tables 2 and 3, the performance of the proposed method is approximately the same as Ref. [14] when the criterion is TPRate, and the proposed method performs better than Ref. [14] when the criterion is TNRate. That is to say, the proposed method improves the classification accuracy for negative samples and maintains the accuracy for positive samples; therefore, the overall classification accuracy for all samples is higher than that with Ref. [14]. For example, with still images, the proposed method has a TNRate of 75.75 % and an accuracy of 89.70 % compared with 63 and 84.50 % using Ref. [14]. Ref. [14] analyzes the color features of flame pixels, and a series of rules is used to limit the range of flame colors in the YCbCr color space. However, this algorithm uses color information only, and false alarm occurs when an object with a flame-like color is present in the images or video, e.g., the red flower, red flag, and red car. Our proposed method uses three features, i.e., color information, saliency, and texture, and the number of false positives is reduced greatly, while the classification accuracy is improved. The proposed method and Ref. [14] are both based on still images; therefore, classification efficiency is not sensitive to camera movements. For most of the test videos, the TPRate values achieved by our proposed method are higher than those obtained by Günay et al. [9] and Habiboˇglu et al. [15]. For example, for Movie4, the TPRate values obtained by Günay et al. [9] and Habiboˇglu et al. [15] are 76.58 and 81.08 %, respectively, whereas the TPRate value achieved by the proposed method is 99.73 %. That is to say, the classification performance of the proposed method for positive samples is better than that of Refs. [9] and [15]. When the criterion is TNRate, the values achieved by our proposed method are worse than Refs. [9] and [15], according to the experimental results. Therefore, the false alarm rate caused by the proposed method should be improved further. For example, for Movie 6, the TNRate values obtained by Günay et al. [9] and Habiboˇglu et al. [15] are 99.04 and 96.15 %, respec-

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Fig. 6 Video clips in experiments. a Movie1, b Movie2, c Movie3, d Movie4, e Movie5, f Movie6

Table 2 Comparison of classification results for still images

Table 3 Comparison of classification results for video sequences

Algorithm

TPRate (%)

TNRate(%)

Accuracy (%)

Criteria (%) Methods Movie

Proposed

99.00

75.75

89.70

Ref. [14]

98.83

63.00

84.50

tively, whereas the TNRate value achieved by the proposed method is 86.54 %. The method Ref. [9] was proposed based on a fixed camera, and two background images were updated in the detection process. Therefore, the performance of this method for videos (i.e., Movie1 and Movie2) with fixed camera is efficient. For a video with few camera movements, e.g., Movie4, the classification efficiency of this method is also acceptable. For example, the accuracy of this method is 86.29 % for Movie4. The method proposed by Günay et al. [15] based on covariance can solve partially the camera movement problem. For example, for videos with camera movement, e.g., Movie4, Movie5, and Movie6, the TPRate values achieved by Günay et al. [15] are all higher than 80 %. On the other hand, higher values of TNRate compared with our proposed method indicates that Ref. [15] can reduce false alarm efficiently for different videos. Generally, the proposal can achieve a balance among TPRate, TNRate, and accuracy. For example, the values 44.82 and 52.90 % achieved by Günay et al. [15] and Habiboˇglu et al. [14], respectively, for Movie 3 and Movie 1 when the criterion is TPRate and TNRate. On the other hand, the values achieved by the proposal are 99.57 and 86.45 %. The experimental results listed in Table 4 are the number of frames that can be analyzed by different methods per second. According to the results listed in Table 4, the speed of Ref. [14] is the fastest among these methods because of its low computational complexity, whereas the speed of Ref. [15] is the slowest because of its high computational complexity. All of these methods can achieve real-time ability according to our experimental results.

TPRate

1

2

3

6

[9]

NaN

90.62

24.53

76.58

18.52 50.51

NaN

100.00 99.79

99.73

99.26 99.19

[15]

NaN

80.63

44.82

81.08

80.56 99.59

100.00 99.57

99.73

99.17 99.39

[9]

100.00 NaN

NaN 100.00

99.14 99.04

[14]

52.90

NaN

71.55 52.88

[15]

100.00 NaN

Proposed 86.45 Accuracy

5

[14]

Proposed NaN TNRate

4

NaN

NaN 100.00 100.00 96.15

NaN

NaN

97.72

24.53

86.29

42.75 58.99

100.00 99.79

88.83

90.93 91.10

44.82

88.93

86.40 99.19

100.00 99.57

98.89

96.63 97.14

[9]

100.00 90.62

[14]

52.90

[15]

100.00 80.63

Proposed 86.45

73.48

90.73 86.54

Table 4 Comparison of computational efficient results for video sequences Number of frames analyzed by different method per second (fps) Video

Frame size

Ref. [9]

Ref. [14]

Ref. [15]

Movie1 Movie2

Proposed

320 × 240

58

121

24

55

400 × 256

50

92

18

42

Movie3

320 × 240

56

98

22

51

Movie4

440 × 328

36

88

20

33

Movie5

440 × 328

33

90

19

32

Movie6

448 × 336

30

91

18

31

5 Conclusion Traditional flame sensors are not suitable for large-scale scenes; therefore, computer vision-based flame detection techniques were developed for these problem domains. Flame areas are exceptional events in an image, and they are always noticeable. For our proposed method, we used various features to extract flame areas from still images and

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videos using a saliency detection technique. To explore the flame color information, the flame color distribution in the YCbCr color space was obtained by Parzen window nonparametric estimation. This prior information was fused with the saliency detection results so that the candidate flame areas could be extracted from still images and video sequences. Next, ULBP was used to analyze the texture features of the flame area. An exponential model was used to describe the ULBP of the flame area, and only two parameters were calculated for classification. The proposed algorithm is based on still images, but it can be extended naturally to video sequences. Temporal information was not considered in the present study, but the algorithm can be applied in situations where camera movement is a serious problem, e.g., parallel movement, shaking, zooming in, and zooming out. In our experiments, we compared the effectiveness of the proposed method with three methods [9,14,15], which includes a similar algorithm proposed by Turgay [14]. According to the experimental results, the classification accuracy of our proposed method was approximately the same as for the Ref. [14] for positive samples, but the number of false positives was reduced greatly with our method. Therefore, the overall classification accuracy of the proposed method was better than the method proposed by Turgay. In the comparison, the proposal achieved a balance in three criterions comparing to other methods. Acknowledgments This research is supported by the National Natural Science Foundation of China (Nos. 61103118 and 61203269), the Shandong Natural Science Foundation of China (No. BS2012DX027).

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