基于LBP金字塔的视频烟雾检测方法

需积分: 35 167 浏览量更新于2024-09-06 1 收藏 1.12MB PDF 举报

本文主要探讨了基于图像处理的烟雾检测方法，特别是在视频监控系统广泛应用的背景下，如何通过改进视频烟雾检测的效率来提高火灾预警的性能。论文标题为"Video-based Smoke Detection with Histogram Sequence of LBP and LBPV Pyramids"，作者是Feiniu Yuann，来自江西财经大学信息科技学院。文章的核心技术是利用了多尺度分析构建的3级图像金字塔（multi-scale analysis and a 3-level image pyramid），这是一种处理视频数据的有效手段，能够捕捉不同尺度下的图像特征，增强对烟雾变化的敏感性。作者选择局部二值模式（Local Binary Patterns, LBP）作为特征提取器，因为LBP具有旋转不变性和光照不变性，这在复杂的监控环境中是非常关键的，它能稳定地识别出烟雾与其他背景元素之间的差异。首先，通过多尺度分析，金字塔结构将原始图像分解为多个不同尺寸的层次，每个层次上的图像能够捕捉到从全局到局部的细节变化。接着，在每一层图像上应用LBP，包括均匀模式、旋转不变模式和旋转不变均匀模式，这样可以从不同的角度提取出稳定的纹理特征。这些特征序列构成的LBP金字塔进一步增强了对烟雾特性的描述，使得算法在不同视角和光照条件下都能保持较高的识别准确性。为了提升检测效率，文中可能还涉及到神经网络技术，可能是用于训练和优化特征分类模型。通过神经网络，可以学习并自动调整权重，以便更准确地区分烟雾信号和非烟雾信号，减少误报和漏报的可能性。此外，文章可能讨论了实验设计、数据集的选择、以及算法性能评估，包括检测精度、召回率和计算效率等方面。通过对比传统的烟雾检测方法，这种方法的优势在于其鲁棒性和经济性，适用于开放或大面积空间的火灾监控。总结来说，这篇文章介绍了一种创新的视频烟雾检测方法，通过结合多尺度分析、LBP特征提取和可能的神经网络技术，旨在提高烟雾检测的可靠性和实时性，为公共安全提供了有力的支持。

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

下载后可阅读完整内容，剩余7页未读，立即下载

肆十二

粉丝: 2w+
资源: 47

基于LBP金字塔的视频烟雾检测方法

基于颜色识别的火焰检测算法的opencv实现

烟雾识别.pdf

基于前景提取的烟雾识别和运动检测

毕业设计(论文)基于单片机的烟雾检测系统.pdf

基于卷积神经网络的野外烟雾检测研究.pdf

基于深度学习的视频火焰烟雾检测方法.pdf

基于单片机的烟雾报警系统.pdf

光电式烟雾检测电路.pdf

基于证据理论和神经网络的烟雾图像检测.pdf

基于物联网技术的智能化电力安全帽烟雾检测系统.pdf

最新资源