Fire Detection Algorithms Using Multimodal ... - Bilkent University

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CHAPTER 2. FLAME DETECTION IN VISIBLE RANGE VIDEO 11and motion recurrence images do not provide a quantitative frequency domainmeasure. On the other hand, wavelet transform is a time-frequency analysis toolproviding both partial frequency and time information about the signal. Onecan examine an entire frequency band in the wavelet domain without completelyloosing the time information [10], [52]. Since the wavelet transform is computedusing a subband decomposition filter bank, it does not require any batch processing.It is ideally suited to determine an increase in high-frequency activity infire and flame colored moving objects by detecting zero crossings of the wavelettransform coefficients.In practice, flame flicker process is time-varying and it is far from being periodic.This stochastic behavior in flicker frequency is especially valid for uncontrolledfires. Therefore, a random model based modeling of flame flicker processproduces more robust performance compared to frequency domain based methodswhich try to detect peaks around 10 Hz in the Fourier domain. In [84], fire andflame flicker is modeled by using HMMs trained with pixel domain features invideo. In this thesis, temporal wavelet coefficients are used as feature parametersin Markov models.Turbulent high-frequency behaviors exist not only on the boundary but alsoinside a fire region. Another novelty of the proposed method is the analysis ofthe spatial variations inside fire and flame colored regions. The method describedin [78] does not take advantage of such color variations. Spatial wavelet analysismakes it possible to detect high-frequency behavior inside fire regions. Variationin energy of wavelet coefficients is an indicator of activity within the region. Onthe other hand, a fire colored moving object will not exhibit any change in valuesof wavelet coefficients because there will not be any variation in fire colored pixelvalues. Spatial wavelet coefficients are also used in Markov models to characterizethe turbulent behavior within fire regions.
CHAPTER 2. FLAME DETECTION IN VISIBLE RANGE VIDEO 122.2 Steps of Video Flame Detection AlgorithmThe proposed video-based fire detection algorithm consists of four sub-algorithms:(i) moving pixels or regions in the current frame of a video are determined, (ii)colors of moving pixels are checked to see if they match to pre-specified fire colors.Afterwards, wavelet analysis in (iii) temporal and (iv) spatial domains are carriedout to determine high-frequency activity within these moving regions. Each stepof the proposed algorithm is explained in detail in the sequel.2.2.1 Moving Region DetectionBackground subtraction is commonly used for segmenting out moving objectsin a scene for surveillance applications. There are several methods in the literature[19], [3], [77]. The background estimation algorithm described in [19]uses a simple IIR filter applied to each pixel independently to update the backgroundand use adaptively updated thresholds to classify pixels into foregroundand background.Stationary pixels in the video are the pixels of the background scene becausethe background can be defined as temporally stationary part of the video.If thescene is observed for some time, then pixels forming the entire background scenecan be estimated because moving regions and objects occupy only some partsof the scene in a typical image of a video. A simple approach to estimate thebackground is to average the observed image frames of the video. Since movingobjects and regions occupy only a part of the image, they conceal a part of thebackground scene and their effect is canceled over time by averaging. Our mainconcern is real-time performance of the system. In Video Surveillance and Monitoring(VSAM) Project at Carnegie Mellon University [19] a recursive backgroundestimation method was developed from the actual image data using l1-norm basedcalculations.Let I(x, n) represent the intensity value of the pixel at location x in the n−thvideo frame I. Estimated background intensity value, B(x, n + 1), at the same
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Chapter 7Conclusion and Future Work
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