Dynamic texture analysis in video with application to flame, smoke and volatile organic compound vapor detection
Author(s)
Advisor
Çetin, A. EnisDate
2009Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
165
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views
42
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downloads
Abstract
Dynamic textures are moving image sequences that exhibit stationary characteristics
in time such as fire, smoke, volatile organic compound (VOC) plumes,
waves, etc. Most surveillance applications already have motion detection and
recognition capability, but dynamic texture detection algorithms are not integral
part of these applications. In this thesis, image processing based algorithms
for detection of specific dynamic textures are developed. Our methods can be
developed in practical surveillance applications to detect VOC leaks, fire and
smoke. The method developed for VOC emission detection in infrared videos
uses a change detection algorithm to find the rising VOC plume. The rising
characteristic of the plume is detected using a hidden Markov model (HMM).
The dark regions that are formed on the leaking equipment are found using a
background subtraction algorithm. Another method is developed based on an
active learning algorithm that is used to detect wild fires at night and close range
flames. The active learning algorithm is based on the Least-Mean-Square (LMS)
method. Decisions from the sub-algorithms, each of which characterize a certain
property of the texture to be detected, are combined using the LMS algorithm to reach a final decision. Another image processing method is developed to detect
fire and smoke from moving camera video sequences. The global motion
of the camera is compensated by finding an affine transformation between the
frames using optical flow and RANSAC. Three frame change detection methods
with motion compensation are used for fire detection with a moving camera. A
background subtraction algorithm with global motion estimation is developed
for smoke detection.
Keywords
VOC leak detectionRANSAC
motion compensation
optical flow
active learning
least-meansquare (LMS) algorithm
hidden Markov models
dynamic textures
computer vision
night-fire detection
smoke detection
flame detection
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