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Compressive sensing based flame detection in infrared videos
- Source :
- 2013 21st Signal Processing and Communications Applications Conference (SIU)
- Publication Year :
- 2013
- Publisher :
- IEEE, 2013.
-
Abstract
- Date of Conference: 24-26 April 2013 In this paper, a Compressive Sensing based feature extraction algorithm is proposed for flame detection using infrared cameras. First, bright and moving regions in videos are detected. Then the videos are divided into spatio-temporal blocks and spatial and temporal feature vectors are exctracted from these blocks. Compressive Sensing is used to exctract spatial feature vectors. Compressed measurements are obtained by multiplying the pixels in the block with the sensing matrix. A new method is also developed to generate the sensing matrix. A random vector generated according to standard Gaussian distribution is passed through a wavelet transform and the resulting matrix is used as the sensing matrix. Temporal features are obtained from the vector that is formed from the difference of mean intensity values of the frames in two neighboring blocks. Spatial feature vectors are classified using Adaboost. Temporal feature vectors are classified using hidden Markov models. To reduce the computational cost only moving and bright regions are classified and classification is performed at specified intervals instead of every frame. © 2013 IEEE.
- Subjects :
- Adaptive boosting
Temporal features
Computational costs
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
A-wavelet transform
Compressive sensing
Vectors
Wavelet transforms
Image processing
Feature extraction algorithms
Infra-red cameras
Flame detection
Compressed sensing
Wavelet transform
Hidden Markov models
Infrared
Spatial feature vector
Infrared radiation
Algorithms
Signal reconstruction
Subjects
Details
- Language :
- Turkish
- Database :
- OpenAIRE
- Journal :
- 2013 21st Signal Processing and Communications Applications Conference (SIU)
- Accession number :
- edsair.od......3533..c9ef6b8bd85edea5d548b3a07767b9d4