Conventional point smoke and fire detectors are widely used in buildings. They typically detect the presence of certain particles generated by smoke and fire by ionization or photometry. Alarm is not issued unless particles reach the sensors to activate them. Therefore, they cannot be used in open spaces and large covered areas. Video based fire detection systems can be useful to detect fire in large auditoriums, tunnels, atriums, etc. The strength of using video in fire detection makes it possible to serve large and open spaces. In addition, closed circuit television (CCTV) surveillance systems are currently installed in various public places monitoring indoors and outdoors. Such systems may gain an early fire detection capability with the use of a fire detection software processing the outputs of CCTV cameras in real time.
Image and video content understanding and analysis methods have been studied by many researchers including [7,10,13,17]. Content based understanding methods have to be designed according to the specific application. Fire detection in video is such an application that needs specific methods. There are several video-based fire and flame detection algorithms in the literature [5,8,11,15,19]. These methods make use of various visual signatures including color, motion and geometry of fire regions. Healey et al. [11] use only color clues for flame detection. Phillips et al. [19] use pixel colors and their temporal variations. Chen et al. [5] utilize a change detection scheme to detect flicker in fire regions. In [8], Fast Fourier Transforms (FFT) of temporal object boundary pixels are computed to detect peaks in Fourier domain. Liu and Ahuja [15] also represent the shapes of fire regions in Fourier domain. An important weakness of Fourier domain methods is that flame flicker is not purely sinusoidal but random. Therefore, it is hard to detect peaks in FFT plots. In addition, Fourier Transform does not carry any time information. In order to make FFTs also carry time information, they have to be computed in windows of data. Hence, temporal window size is very important for detection. If the window size is too long, then one may not observe peakiness in the FFT data. If it is too short, then one may completely miss cycles and therefore no peaks can be observed in the Fourier domain.
We developed a wavelet based fire detection method in video. Our method not only detects fire and flame colored moving regions in video but also analyzes the motion of such regions in wavelet domain for flicker estimation. It is observed that turbulent flames flicker with a characteristic flicker frequency of around 10 Hz independent of the burning material and the burner [1,4]. The appearance of an object whose contours, chrominance or luminosity values oscillate at a frequency greater than 0.5 Hz is an important sign of the possible presence of flames [8]. Therefore, fire detection schemes can be made more robust to false alarms by detecting periodic high-frequency behavior in flame colored moving pixels.
High-frequency analysis of moving pixels is carried out in wavelet domain in our work. There is an analogy between our motion analysis in wavelet domain and the temporal templates of [7] and the motion recurrence images of [13]. Wavelet transform is a time-frequency analysis tool, and one can examine an entire frequency band in the wavelet domain without completely loosing the time information [3,16]. Since the wavelet transform is computed using a subband decomposition filter bank, it does not require any batch processing. It is ideally suited to determine an increase in high-frequency activity in fire and flame colored moving objects by detecting zero crossings of the wavelet transform coefficients.
Turbulent high-frequency behaviors exist not only on the boundary but also inside a fire region. Another novelty of the proposed method is the analysis of the spatial variations inside fire and flame colored regions. The method described in [8], does not take advantage of such color variations. Spatial wavelet analysis makes it possible to detect high-frequency behavior inside fire regions. Variation in energy of wavelet coefficients is an indicator of activity within the region. On the other hand, a fire colored moving object will not exhibit any change in values of wavelet coefficients because there will not be any variation in fire colored pixel values.