An Early Fire Detection Algorithm Using IP Cameras

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.

An optimal many-core model-based supercomputing for accelerating video-equipped fire detection

“…Thus, color spaces, such as YCbCr, YUV, and CIE Lab, can be promising candidates for the purpose of fire-like region detection, in which the chrominance components (Cb and Cr in the YCbcCr color model, U and V in the YUV color model, a and b in the CIE Lab color model) and luminance component (Y in both YCbCr and YUV color models, L in the CIE Lab color model) of a movie frame can be processed independently. According to [45][46][47], the YCbCr color space is more effective for distinguishing luminance information from chrominance information than other color spaces. Thus, the YCbCr color space is used for the multi-stage fire detection algorithm.…”

An Autonomous UAS with AI for Forest Fire Prevention, Detection, and Real Time Advice and Communication To and Among Firefighters

“…The probability of misclassification of this system is high. Since then many other research have been published in the area of fire detection recommends conventional cameras installed on towers to provide a continuous sequence of video frames of an area triggering an alert in case of fire [2]. The systems consist of the imagers, a computer for image processing, the software, and a communication network to transmit the information.…”

Approach to Detecting Forest Fire by Image Processing Captured from IP Cameras

“…In this paper, the author not establish a new algorithm for image processing, they use the combination of many algorithms that are applying in detecting forest fire in the world [2,4]. Including:…”

“…However, to efficiently use the IP technology for fire detection purposes, the smoke detection algorithm must perform directly in the Discrete Cosine Transform (DCT) domain, because decoding (from DCT domain to spatial domain) and possible encoding (from spatial domain to DCT domain) are considerably high time consuming processes. Moreover, almost all fire detection algorithms including those proposed in [2,5,6] are carried out in the spatial domain, analyzing the value of each pixel or block of pixels. Therefore, any implementation of these algorithms in IP technology requires considerably high extra processing time.…”