Dynamic texture analysis in video with application to flame, smoke and volatile organic compound vapor detection
Çetin, A. Enis
Item Usage Stats
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.
KeywordsVOC leak detection
least-meansquare (LMS) algorithm
hidden Markov models
Showing items related by title, author, creator and subject.
Verstockt, S.; Hoecke, S. V.; Beji, T.; Merci, B.; Gouverneur, B.; Çetin, A. Enis; Potter, P. D.; Walle, R. V. D. (Elsevier, 2013)In this paper a novel multi-modal flame and smoke detector is proposed for the detection of fire in large open spaces such as car parks. The flame detector is based on the visual and amplitude image of a time-of-flight ...
Acar, Can; Atlas, Arda; Çevik, Koray; Ölmez İsa; Ünlü, Mustafa; Özkan, Derya; Duygulu, Pınar (IEEE, 2007)People are the most important subjects in news videos and for proper retrieval of people images; face detection is a very crucial step. However, face detection and recognition in news videos is a very challenging task due ...
Habiboǧlu, Y.H.; Günay, Osman; Çetin, A. Enis (IEEE, 2011)Video fire detection system which uses a spatio-temporal covariance matrix of video data is proposed. This system divides the video into spatio-temporal blocks and computes covariance features extracted from these blocks ...