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烟雾检测

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Video-based smoke detection with histogram sequence

of LBP and LBPV pyramids

Feiniu Yuan

School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, Jiangxi, China

article info

Article history:

Received 4 January 2010

Received in revised form

31 October 2010

Accepted 5 January 2011

Available online 20 January 2011

Keywords:

Video-based smoke detection

Local binary pattern

Multi-scale analysis

Neural network

abstract

Video surveillance systems are widely applied in a variety of ﬁelds. Hence, video-based smoke

detection is regarded as an effective and inexpensive way for ﬁre detection in an open or large spaces.

In order to improve the efﬁciency of the video-based smoke detection, a novel video-based smoke

detection method is proposed by using a histogram sequence of pyramids. The method involves four

steps. Firstly, through multi-scale analysis, a 3-level image pyramid is constructed. Secondly, local

binary patterns (LBP), which are insensitive to image rotation and illumination conditions, are

extracted at each level of the image pyramid with uniform pattern, rotation invariance pattern and

rotation invariance uniform pattern to generate an LBP pyramid. Thirdly, local binary patterns based on

variance (LBPV) with the same patterns are also adopted in the same way to generate an LBPV pyramid.

And fourthly, histog rams of the LBP and LBPV pyramids are computed, and then all the histograms are

concatenated into an enhanced feature vector. A neural network classiﬁer is trained and used for

discrimination of smoke and non-smoke objects. Experimental results show that the features are

insensitive to rotation and illumination, and that the method is feasible and effective for video-based

smoke detection at interactive frame rates.

1. Introduction

Traditional smoke detectors usually detect the presence of

combustion products through an ionization or photometry based

sensors. But it takes a long time for combustion products to reach

these sensors in outdoor or open spaces and in the case of a strong

wind, combustion products may even be blown away, thus failing

to give ﬁre alarms. Therefore, traditional smoke detectors are not

suitable in such cases. In recent years, researchers have used

computer vision technology in the ﬁeld of ﬁre detection in order

to overcome the aforementioned deﬁciencies of traditional sen-

sors. Video-based ﬁre detection is one of the computer vision-

based methods and can be classiﬁed into two categories: video-

based ﬂame and video-based smoke detections.

1.1. Video ﬁre and smoke detection

Many approaches to the video-based ﬂame detection have

been discussed by different worldwide researchers. Yamagishi

and Yamaguchi [1] presented a ﬂame detection algorithm based

on the spatio-temporal ﬂuctuation data of ﬂame contours. Noda

and Ueda [2] proposed a ﬂame detection system based on gray

scale images for tunnels. Phillips III et al. [3] implemented a video

ﬂame detection method, using a Gaussian-smoothed color histo-

gram. T

oreyin et al. [4] presented the video ﬂame detection

method based on motion, ﬂicker, edge blurring and color features.

Yuan et al. [5] proposed a video ﬂame detection method, using the

mixture Gaussian model to extract temporal features. Ko et al. [6]

presented a ﬁre detection method using techniques of moving

detection and ﬁre-colored pixels.

In many cases of ﬁre, smoke is usually visible before the ﬂame

can be sighted, so video-based smoke detection is able to give ﬁre

alarms earlier than the video-based ﬂame detection. Most meth-

ods of video-based smoke detection often extract motion, edge,

color and texture features from video for the discrimination of

smoke and non-smoke objects. Toreyin et al. [7] used the features

of motion, ﬂicker, edge blurring and color to detect smoke. In the

method presented by Gubbi et al. [8], some statistical features,

such as arithmetic mean, geometric mean, standard deviation,

skewness, kurtosis and entropy, were computed on each sub-

band of 3-level wavelet transformed images. Then, the SVM light

implementation of support vector machines was used for detec-

tion of smoke. Guillemant and Vicente [9] proposed a method of

smoke detection applied in the forests. Ferrari et al. [10] proposed

a real-time image processing technique for the detection of steam

in videos. They used Hidden Markov Tree (HMT), which was

derived from the coefﬁcients of the dual-tree complex wavelet

transform (DT-CWT) in small local regions, to characterize the

steam texture pattern, and an SVM classiﬁer was used to detect

the steam. This approach has the referential signiﬁcance to smoke

Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/ﬁresaf

Fire Safety Journ al

doi:10.1016/j.ﬁresaf.2011.01.001

Tel.: +86 7913820232.

E-mail address: yfn@ustc.edu

Fire Safety Journal 46 (2011) 132–139

detection, because smoke has characteristics similar to steam.

Gottuk et al. [11] evaluated the effectiveness of several commer-

cial video-based ﬁre detection systems for small spaces on navy

ships. Their experiments showed that the video-based ﬁre detec-

tion systems detected more ﬁres faster than the traditional

systems. Yuan [12] adopted the integral image technique to

quickly estimate the motion of moving objects and proposed an

accumulative motion model for the video smoke detection. To

reduce false alarms, the orientation is accumulated over time to

compensate the results for the inaccuracy of orientation. Han and

Lee [13] presented a ﬂame and smoke detection method to be

used in tunnels by analyzing color and motion information.

1.2. Texture analysis

Texture analysis is an effective method for the smoke detec-

tion. Most of the existing methods are sensitive to rotation and

illumination. Although some methods of texture analysis, such as

co-occurrence matrix methods [14], are insensitive to rotation, it

depends greatly on illumination conditions. Histogram equaliza-

tion is often performed to reduce the adverse effects of varying

illumination. However, histogram equalization may decrease

other characteristics of images. Ojala et al. [15] presented local

binary patterns (LBP) for rotation and illumination invariant

texture classiﬁcation. Huang et al. [16] improved this method

by computing the derivative based LBPs for face alignment. To

prevent loss of global information caused by an LBP, Jafari-

Khouzani and Soltanian-Zadeh [17] used the radon transform to

estimate the principal orientation of the texture image and then

computed the wavelet energy features along the principal orien-

tation. The radon transform though is time consuming. Generally,

texture can be precisely characterized by a spatial statistical

measure and image contrast. So Ojala et al. [15] used the LBP

and variance joint histogram for rotation invariant texture classi-

ﬁcation. Guo et al. [18] observed that a quantization step is

required due to the continuity attribute of the variance value.

The quantization step and the computation of the joint histogram

were completely avoided by proposing a new operator, which is

called the local binary pattern variance (LBPV). In fact, this

operator can be regarded as the integral projection along the

variance axis. Liao et al. [19] proposed dominant local binary

patterns for texture classiﬁcation by regarding the ﬁrst 80% most

frequent patterns as dominant features.

In this paper, we make use of the histogram sequence of LBP

and LBPV pyramids to propose a new approach to the video-based

smoke detection. The LBP and LBPV features, which were pre-

sented by Ojala et al. [15] and Guo et al. [18], respectively, are

computed at each level of the 3-level image pyramid with

uniform, rotation-invariance and rotation-invariance-uniform

patterns. Then, all the histograms of the LBP and LBPV pyramids

are concatenated into a feature vector for smoke detection. Hence,

the feature vector contains both local and global informations.

This paper presents at least two innovative ideas on the video-

based smoke detection. First, LBP-related texture analysis meth-

ods are used to detect smoke. Second, different LBP patterns are

used at different levels of the image pyramid, in order to collect

local and global informations.

2. Local binary pattern

2.1. LBP

Local binary pattern (LBP) is a gray-scale texture operator,

which can capture spatial characteristics of images. At a center

pixel, a pattern number is computed by comparing its value with

values of its neighboring pixels. The LBP initial label for that

center pixel is given by

LBP

P, R

P1

i ¼ 0

sðg

g

Þ2

ð1Þ

sðxÞ¼

1ifxZ 0

0 else



ð2Þ

where g

is the gray scale value of the center pixel, g

is the value

of its ith neighbor, P is the number of neighbors and R is the

radius of the neighborhood which is the Euclidean distance

between the center point and its neighbors.

Fig. 1 shows examples of circularly symmetric neighbor sets

for different P and R. LBP

8,1

, LBP

8,2

and LBP

16,2

denote a point set

with P¼8 and R¼1, one with P¼8 and R¼ 2, and one with P¼16

and R ¼2, respectively.

As the radius increases, an LBP contains more global informa-

tion of texture patterns and also needs more neighbors, thus

increasing the computational cost. If a large radius R is mixed

with small P, it will result in severe artifacts in the re-sampling. So

in our smoke detection, for the sake of balance between informa-

tion and computation, the number of neighbors is set to 8, and the

radius is set to 1.

Circularly symmetric neighbor sets require bi-linear interpola-

tion of pixel values. It may take a lot of time to interpolate values

of all the neighbors. In order to further improve the computa-

tional performance, the Euclidean distance is replaced with the

block distance, as shown in Fig. 2. Thus, re-sampling of the values

of the neighbors is completely avoided.

Ojala et al. [15] deﬁned three different types of patterns, which

are uniform, rotation-invariant and rotation-invariant-uniform.

Fig. 1. LBP texture operator.

Fig. 2. Modiﬁed LBP texture operator with block distance (LBP

8,1

F. Yuan / Fire Safety Journal 46 (2011) 132–139 133

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