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A vision-based approach to fire detection (Finding (classified image…
A vision-based
approach to
fire detection
Pedro Gomes
Pedro Santana
Jose Barata
( 18 June 2014)
Finding
classified image pixel according to an appearance model of fire.
RGB [1–4],
YCbCr [5], .
], CIE L
a
b* [6] .
HSI [7] colour spaces.
typical fire’s dynamic texture
spatio-temporal wavelet analysis
to exploit the well-known flickering
and textured characteristics of flames for their detection.
vision systems need
handle exceptions
manage the speed-accuracy trade-off
avoid perceptual aliasing situations
embedded with seamless calibration procedures
challenges
handling sudden background changes
determining when a computationally intensive frequency analysis is worth applying
detecting and tracking potential
distractors
people with fire-coloured clothing
automatically learning the camera-world coordinates
mapping
technique
employing a dynamic threshold to the magnitude of each pixel’s intensity variation across three magnitude of each pixel’s intensity variation across three
fire detection pipeline
segmenting fire regions according to a colour model
determining which of the segmented regions present
a dynamic texture
filtering out the regions with dynamic texture that do not exhibit the spatio-temporal frequency signature of typical fire
fire confirmation pipeline
-reduce the fire false alarm rate
detects foreground objects invariant to the presence of shadows
tracks the objects across frames
recognizes the objects’ categories
coordiate with GPS
detect distractor
boundary for thescene and area of subject
fire detection
color based analysis
color space
HSI
RGB
YCbCr
method
HYR method
HY method
more consistent
Parameter/ Situation
indoor
rural
urban
night
detect fire or non-fire binary image
Spatio-temporal Frequency Analysis
main characteristics of fire is its flickering rate
at a frequency of around 10 Hz
presence of fire signature when 10 Hz
two conditions must be met
First, the ratio of the analysed pixels that were labelled as fire must be above a given threshold
he accumulated number of zero-crossings in
the filters’ outputs must be above a another given
threshold
stage filter bank
Dynamic Textures Detection