The Experts below are selected from a list of 11667 Experts worldwide ranked by ideXlab platform
Nicholas James True - One of the best experts on this subject based on the ideXlab platform.
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Real-time Fire Detection in low quality video
2013Co-Authors: Nicholas James TrueAbstract:For over fifty years, simple smoke and heat sensors have been the primary means of automated Fire Detection. We are now at the point where computer processing power is cheap enough and machine vision technology is sophisticated enough for a new generation of automated Fire Detection systems : video-based Fire Detection (VBFD). While current smoke and Fire Detection technology has proven to be reliable and effective, VBFD technology promises to go where existing systems can't and to detect Fires faster than its venerable predecessors ever could. This thesis explores a few methods for achieving real-time video-based Fire Detection in low quality data. Assuming a stationary source camera, we describe an algorithm that uses a support vector machine to classify short, targeted video sequences as Fire/non-Fire. The algorithm achieves a classification rate of 96.0% on a holdout set of real world data. Furthermore, the system is robust with respect to the distance from the Fire source, works day or night, and only requires the processing power of a common desktop computer
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Real-time Fire Detection in low quality video - eScholarship
2010Co-Authors: Nicholas James TrueAbstract:For over fifty years, simple smoke and heat sensors have been the primary means of automated Fire Detection. We are now at the point where computer processing power is cheap enough and machine vision technology is sophisticated enough for a new generation of automated Fire Detection systems : video-based Fire Detection (VBFD). While current smoke and Fire Detection technology has proven to be reliable and effective, VBFD technology promises to go where existing systems can't and to detect Fires faster than its venerable predecessors ever could. This thesis explores a few methods for achieving real-time video-based Fire Detection in low quality data. Assuming a stationary source camera, we describe an algorithm that uses a support vector machine to classify short, targeted video sequences as Fire/non-Fire. The algorithm achieves a classification rate of 96.0% on a holdout set of real world data. Furthermore, the system is robust with respect to the distance from the Fire source, works day or night, and only requires the processing power of a common desktop computer
Zhang Zheng - One of the best experts on this subject based on the ideXlab platform.
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Fire Detection technology based on support vector machine
Microcomputer & its Applications, 2010Co-Authors: Zhang ZhengAbstract:Concerning the current deficiencies of Fire Detection,a Fire Detection technology based on support vector machine was proposed.Firstly,the suspected area of Fire flame was extracted based on HSI color model,then five main characteristics of early Fire flame were obtained based on image processing technology,and use support vector machine technology for Fire Detection finally.Matlab simulation results showed that Fire Detection technology based on support vector machine had high recognition rate,it overcame the disadvantages of neural network such as over learning,being easily trapped in local minimum,etc.The technology has important theoretical and practical value in the field of Fire Detection.
Narendra Ahuja - One of the best experts on this subject based on the ideXlab platform.
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vision based Fire Detection
International Conference on Pattern Recognition, 2004Co-Authors: Narendra AhujaAbstract:Vision based Fire Detection is potentially a useful technique. With the increase in the number of surveillance cameras being installed, a vision based Fire Detection capability can be incorporated in existing surveillance systems at relatively low additional cost. Vision based Fire Detection offers advantages over the traditional methods. It will thus complement the existing devices. In this paper, we present spectral, spatial and temporal models of Fire regions in visual image sequences. The spectral model is represented in terms of the color probability density of Fire pixels. The spatial model captures the spatial structure within a Fire region. The shape of a Fire region is represented in terms of the spatial frequency content of the region contour using its Fourier coefficients. The temporal changes in these coefficients are used as the temporal signatures of the Fire region. Specifically, an auto regressive model of the Fourier coefficient series is used. Experiments with a large number of scenes show that our method is capable of detecting Fire reliably.
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ICPR (4) - Vision based Fire Detection
2004Co-Authors: Narendra AhujaAbstract:Vision based Fire Detection is potentially a useful technique. With the increase in the number of surveillance cameras being installed, a vision based Fire Detection capability can be incorporated in existing surveillance systems at relatively low additional cost. Vision based Fire Detection offers advantages over the traditional methods. It will thus complement the existing devices. In this paper, we present spectral, spatial and temporal models of Fire regions in visual image sequences. The spectral model is represented in terms of the color probability density of Fire pixels. The spatial model captures the spatial structure within a Fire region. The shape of a Fire region is represented in terms of the spatial frequency content of the region contour using its Fourier coefficients. The temporal changes in these coefficients are used as the temporal signatures of the Fire region. Specifically, an auto regressive model of the Fourier coefficient series is used. Experiments with a large number of scenes show that our method is capable of detecting Fire reliably.
Xiaolin Guo - One of the best experts on this subject based on the ideXlab platform.
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Forest Fire Detection system based on wireless sensor network
2009 4th IEEE Conference on Industrial Electronics and Applications, 2009Co-Authors: Junguo Zhang, Shengbo Liu, Zhongxing Yin, Wenbin Li, Xiaolin GuoAbstract:—As we all know, the forest is considered as one of the most important and indispensable resources, the prevention and Detection of the forest Fire, have been researched hotly in worldwide Forest Fire Prevention Departments. Based on the deficiencies of conventional forest Fire Detection on real time and monitoring accuracy, the wireless sensor network technique for forest Fire Detection was introduced, together with satellite monitoring, aerial patrolling and manual watching ,an omni-bearing and stereoscopic air and ground forest-Fire Detection pattern was found so that the decision for Fire-extinguishing or Fire prevention can be made rightly and real-timely by related government departments. A cluster-based wireless sensor network paradigm for forest Fire real-time Detection was put forward in this paper. Some key questions were discussed emphatically, such as the ad hoc network related technology, the node hardware designing, the forest-Fire forecasting model and the propagation characteristic of UHF wireless signal and so on. Index Terms—forest Fire Detection, wireless sensor network, ad hoc network, radio wave attenuations, wireless communication I. THE IMPORTANCE OF WIRELESS SENSOR NETWORK TO FOREST Fire Detection A. The Importance of Forest Fire Detection The forest is considered as one of the most important and indispensable resource, furthermore, as the protector of the earth's ecological balance .However, forest Fire, affected by some human uncontrolled behavior in social activities and abnormal natural factors, occurs occasionally. Forest Fire was considered as one of the severest disasters, which destroyed forest resources safety and threatened human-living environment. Recently, with the affection of factors such as climatic fluctuations, human activities, etc, a tendency of intense increase was showed in the forest Fire. Accordingly, a significant hotspots to the prevention and Detection of the forest Fire, has been researched in worldwide Forest Fire Prevention Departments [1-3]. B. Forest Fire Detection System Based on Wireless Sensor Network At present, traditional forest Fire prevention measures were ground patrolling, watching tower, aerial prevention, long-distance video Detection and satellite monitoring and so on.
Ma Zong-fang - One of the best experts on this subject based on the ideXlab platform.
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Image Fire Detection algorithm based on support vector machine
Journal of Computer Applications, 2010Co-Authors: Ma Zong-fangAbstract:Concerning the shortcomings of traditional Fire Detection,an image Fire Detection algorithm based on Support Vector Machine(SVM)was presented,and compared with the image Fire Detection based on neural network.The results show that the presented algorithm overcame the disadvantages of neural network such as over learning,being easily trapped in local minimum,etc.,and reduced the complexity of doing a lot of experiments and statistical analysis to obtain recognition threshold.The experimental results show that the image Fire Detection algorithm based on SVM has higher accuracy,and it is effective to solve the recognition with small samples,multi-dimension and nonlinear property.