Researchers extensively studied 2-D textures and related problems in
the field of image processing. On the other hand, there is very little
research on three-dimensional (3-D) texture detection in video. Trees,
fire, smoke, fog, sea, waves, sky, and shadows are examples of
time-varying 3-D textures in video. It is well known that tree leaves in
the wind, moving clouds etc. cause major problems in outdoor video
motion detection systems. If one can initially identify bushes, trees,
and clouds in a video, then such regions can be excluded from the search
space or proper care can be taken in such regions, and this leads to
robust moving object detection and identification systems in outdoor
video. Other practical applications include early fire detection in
tunnels, large rooms, atriums and forests; wave-height detection,
automatic fog alarm signaling in intelligent highways and tunnels. One
can take advantage of the research in 2-D textures to model the spatial
behaviour of a given 3-D texture. Additional research has to be carried
out to model the temporal variation in a 3-D texture. For example, a
1960’s mechanical engineering paper claims that flames flicker with a
frequency of 10 Hz. However, we experimentally observed that flame
flicker process is not a narrow-band activity but it is wide-band
activity covering 2 to 15 Hz. Zero-crossings of wavelet coefficients
covering the band of 2 to 15 Hz is an effective feature and Hidden
Markov Models (HMM) can be trained to detect temporal characteristics of
fire using the wavelet domain data. Similarly, temporal behaviour of
tree leaves in the wind or cloud motions should be investigated to
achieve robust video understanding systems including content based video
retrieval systems. |
Overview
