Abstract
The presence of smoke is the first symptom of fire; therefore to achieve early fire detection, accurate and quick estimation of the presence of smoke is very important. In this paper we propose an algorithm to detect the presence of smoke using video sequences captured by Internet Protocol (IP) cameras, in which important features of smoke, such as color, motion and growth properties are employed. For an efficient smoke detection in the IP camera platform, a detection algorithm must operate directly in the Discrete Cosine Transform (DCT) domain to reduce computational cost, avoiding a complete decoding process required for algorithms that operate in spatial domain. In the proposed algorithm the DCT Inter-transformation technique is used to increase the detection accuracy without inverse DCT operation. In the proposed scheme, firstly the candidate smoke regions are estimated using motion and color smoke properties; next using morphological operations the noise is reduced. Finally the growth properties of the candidate smoke regions are furthermore analyzed through time using the connected component labeling technique. Evaluation results show that a feasible smoke detection method with false negative and false positive error rates approximately equal to 4% and 2%, respectively, is obtained.
Highlights
Fire detection can help to alert of and prevent disasters that generate great economic damages and human losses
The combustion of objects usually begins with the emission of smoke, even before catching fire; the presence of smoke is an essential factor for early fire detection
In this paper we have proposed an early fire detection scheme using Internet Protocol (IP) camera technology with Motion JPEG (MJPEG) codec, in which the Discrete Cosine Transform (DCT)
Summary
Fire detection can help to alert of and prevent disasters that generate great economic damages and human losses. The video processing-based fire detection algorithms are carried out using two principal characteristics of fire, which are flame and smoke. In [2], authors proposed a method for fire detection using a multilayer neural network (MNN) with a back-propagation algorithm, which is trained using the color property of flames presented in the HSI (Hue-Saturation-Intensity) color space. This algorithm analyses the color of each pixel to determine if some pixels present the flame features or not. The proposed algorithm detects the presence of smoke using several smoke features, such as color, motion and spreading characteristics, which are extracted directly from DCT coefficients to avoid the decoding process.
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