Video processing algorithms for wildfire surveillance
Author(s)
Advisor
Çetin, A. EnisDate
2015-05Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
We propose various image and video processing algorithms for wild re surveillance.
The proposed methods include; classi er fusion, online learning, real-time
feature extraction, image registration and optimization. We develop an entropy
functional based online classi er fusion framework. We use Bregman divergences
as the distance measure of the projection operator onto the hyperplanes describing
the output decisions of classi ers. We test the performance of the proposed
system in a wild re detection application with stationary cameras that scan prede
ned preset positions. In the second part of this thesis, we investigate di erent
formulations and mixture applications for passive-aggressive online learning algorithms.
We propose a classi er fusion method that can be used to increase the
performance of multiple online learners or the same learners trained with di erent
update parameters. We also introduce an aerial wild re detection system to test
the real-time performance of the analyzed algorithms. In the third part of the
thesis we propose a real-time dynamic texture recognition method using random
hyperplanes and deep neural networks. We divide dynamic texture videos into
spatio-temporal blocks and extract features using local binary patterns (LBP).
We reduce the computational cost of the exhaustive LBP method by using randomly
sampled subset of pixels in the block. We use random hyperplanes and
deep neural networks to reduce the dimensionality of the nal feature vectors.
We test the performance of the proposed method in a dynamic texture database.
We also propose an application of the proposed method in real-time detection of
ames in infrared videos. Using the same features we also propose a fast wild re
detection system using pan-tilt-zoom cameras and panoramic background subtraction.
We use a hybrid method consisting of speeded-up robust features and
mutual information to register consecutive images and form the panorama. The
next step for multi-modal surveillance applications is the registration of images
obtained with di erent devices. We propose a multi-modal image registration
algorithm for infrared and visible range cameras. A new similarity measure is described using log-polar transform and mutual information to recover rotation
and scale parameters. Another similarity measure is introduced using mutual information
and redundant wavelet transform to estimate translation parameters.
The new cost function for translation parameters is minimized using a novel lifted
projections onto convex sets method.
Keywords
Projections onto convex setsClassi er fusion
Online learning
Entropy maximization
Wild re detection
Adaptive ltering
LMS
Bregman divergence
Image processing
Infrared
Mutual information
Wavelet transform
Image registration
Log-polar transform
Supporting hyperplanes