Real-time Fire Detection Method Combining AdaBoost, LBP and Convolutional Neural Network in Video Sequence

被引：0

作者：

Maksymiv, Oleksii ^{[1
]}

Rak, Taras ^{[1
]}

Peleshko, Dmytro ^{[2
]}

机构：

[1] Lviv State Univ Life Safety, Kleparivska Str 35, Lvov, Ukraine

[2] Lviv Polytech Natl Univ, S Bandery Str 12, Lvov, Ukraine

来源：

2017 14TH INTERNATIONAL CONFERENCE: THE EXPERIENCE OF DESIGNING AND APPLICATION OF CAD SYSTEMS IN MICROELECTRONICS (CADSM) | 2017年

关键词：

computer vision; fire detection; smoke detection; Adaboost; local binary pattern; convolutional neural network;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents a novel algorithm for detection certain types of emergencies relating to fire, smoke and explosions by processing the data recorded from the camera monitoring, based on cascaded approach. First, the combination of Adaboost and Local binary pattern (LBP) are using for getting Region of Interest (ROI) and reducing time complexity. Next, to alleviate common problems of vulnerable such as false positive, we propose to use Convolutional Neural Network (CNN). The final experimental results showed that the accuracy rate of this method for emergencies detection could reach 95.2%.

引用

页码：351 / 353

页数：3

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