Rapid Early Fire Smoke Detection System Using Slope Fitting in Video Image Histogram

2019 ◽  
Vol 56 (2) ◽  
pp. 695-714
Author(s):  
Haifeng Wang ◽  
Yi Zhang ◽  
Xin Fan
Keyword(s):  
Detection System ◽  
Video Image ◽  
Smoke Detection ◽  
Image Histogram ◽  
Fire Smoke ◽  
Fitting In

Research on Fire Smoke Detection Method Based on Video Image

2021 ◽  
Author(s):  
Xiaobo Xu ◽  
Guoxuan Tang ◽  
Jiayi Wu ◽  
Changzhou Geng
Keyword(s):  
Detection Method ◽  
Video Image ◽  
Smoke Detection ◽  
Fire Smoke

scholarly journals Nighttime Fire/Smoke Detection System Based on a Support Vector Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Chao-Ching Ho
Keyword(s):  
Support Vector Machine ◽  
Detection System ◽  
Time Algorithm ◽  
Detection Methods ◽  
Support Vector ◽  
Smoke Detection ◽  
Fire Smoke ◽  
Large Fires ◽  
Monitoring Zone

Currently, video surveillance-based early fire smoke detection is crucial to the prevention of large fires and the protection of life and goods. To overcome the nighttime limitations of video smoke detection methods, a laser light can be projected into the monitored field of view, and the returning projected light section image can be analyzed to detect fire and/or smoke. If smoke appears within the monitoring zone created from the diffusion or scattering of light in the projected path, the camera sensor receives a corresponding signal. The successive processing steps of the proposed real-time algorithm use the spectral, diffusing, and scattering characteristics of the smoke-filled regions in the image sequences to register the position of possible smoke in a video. Characterization of smoke is carried out by a nonlinear classification method using a support vector machine, and this is applied to identify the potential fire/smoke location. Experimental results in a variety of nighttime conditions demonstrate that the proposed fire/smoke detection method can successfully and reliably detect fires by identifying the location of smoke.

IEEE 1451-Based Sensor Interfacing and Data Fusion for Fire Smoke Detection

2014 ◽  
Vol 613 ◽  
pp. 219-227
Author(s):  
Chao Ching Ho ◽  
Dan Wen Kuo
Keyword(s):  
Video Processing ◽  
Detection System ◽  
Fire Detection ◽  
Sensor System ◽  
Sensor Data ◽  
Smoke Detection ◽  
Fire Smoke ◽  
Ieee 1451 ◽  
Smart Transducer ◽  
Transducer Interface

The performance of a fire sensor has a significant effect on fire detection. Today’s fire alarm systems, such as smoke and heat sensors, however are generally limited to a close proximity to the fire; and cannot provide additional information about fire circumstances. Thus, it is essential to design a suite of low-cost networked sensors that provide the capability of performing distributed measurement and control in real time. In this work, a wireless sensor system was developed for fire detection. The purpose of this paper is to analyze the integration of traditional fire sensors into intelligent fire management systems by using the smart transducer concept. An automated video processing sensor for fire smoke monitoring applications is integrated into an surveillance network as a case study and supported sensor fusion assessment to improve the resistance to nuisance alarms. The proposed sensor system for fire detection was developed to reconcile issues related to proliferation and interoperability, and the architecture can support a smart transducer interface (IEEE 1451). The proposed embedded system for STIM (smart transducer interface module) and NCAP (network capable application processor) will be implemented with DSP. To realize the self-identification of transducers and plug-and-play connections, a transducer electronic data sheet (TEDS) is also stored inside the DSP. The acquired sensor data are pre-processed and applied to discriminate nuisance sources. The IEEE 1451 standard has been integrated into an automatic video-based fire smoke detection system. The proposed architecture has been tested on an experimental setup with the purpose of monitoring fire incidents successfully.

Traffic incident detection system based on video image processing

2008 ◽  
Vol 28 (7) ◽  
pp. 1886-1889 ◽  
Author(s):  
Qin WANG ◽  
Shan HUANG ◽  
Hong-bin ZHANG ◽  
Quan YANG ◽  
Jian-jun ZHANG
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Video Image ◽  
Incident Detection ◽  
Traffic Incident ◽  
Video Image Processing

Fire smoke detection based on target-awareness and depthwise convolutions

2021 ◽  
Author(s):  
Yunji Zhao ◽  
Haibo Zhang ◽  
Xinliang Zhang ◽  
Xiangjun Chen
Keyword(s):  
Smoke Detection ◽  
Fire Smoke
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