Fire detection in infrared video using wavelet analysis

A novel method to detect flames in infrared (IR) video is proposed. Image regions containing flames appear as bright regions in IR video. In addition to ordinary motion and brightness clues, the flame flicker process is also detected by using a hidden Markov model (HMM) describing the temporal behavior. IR image frames are also analyzed spatially. Boundaries of flames are represented in wavelet domain and the high frequency nature of the boundaries of fire regions is also used as a clue to model the flame flicker. All of the temporal and spatial clues extracted from the IR video are combined to reach a final decision. False alarms due to ordinary bright moving objects are greatly reduced because of the HMM-based flicker modeling and wavelet domain boundary modeling. © 2007 Society of Photo-Optical Instrumentation Engineers

Computer vision based method for real-time fire and flame detection

This paper proposes a novel method to detect fire and/or flames in real-time by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker is detected by analyzing the video in the wavelet domain. Quasi-periodic behavior in flame boundaries is detected by performing temporal wavelet transform. Color variations in flame regions are detected by computing the spatial wavelet transform of moving fire-colored regions. Another clue used in the fire detection algorithm is the irregularity of the boundary of the fire-colored region. All of the above clues are combined to reach a final decision. Experimental results show that the proposed method is very successful in detecting fire and/or flames. In addition, it drastically reduces the false alarms issued to ordinary fire-colored moving objects as compared to the methods using only motion and color clues. © 2005 Elsevier B.V. All rights reserved

HMM based falling person detection using both audio and video [Ses ve video i̇şaretlerinde sakli markof modeli tabanli düşen kişi tespiti]

Automatic detection of a falling person in video is an important problem with applications in security and safety areas including supportive home environments and CCTV surveillance systems. Human motion in video is modeled using Hidden Markov Models (HMM) in this paper. In addition, the audio track of the video is also used to distinguish a person simply sitting on a floor from a person stumbling and falling. Most video recording systems have the capability of recording audio as well and the impact sound of a falling person is also available as an additional clue. Audio channel data based decision is also reached using HMMs and fused with results of HMMs modeling the video data to reach a final decision. © 2006 IEEE

Real-time smoke and flame detection in video [Videoda gerçek zamanda duman ve alev tespiti]

A novel method to detect smoke and/or flame by processing the video data generated by an ordinary camera monitoring a scene is proposed. It is assumed the camera is stationary. Since the smoke is semi-transparent, edges of image frames start loosing their sharpness and this leads to a decrease in the high frequency content of the image. To determine the smoke, the background of the scene is estimated and decrease of high frequency energy of the scene is monitored using the spatial wavelet transforms of the current and the background images. For the detection of flames, in addition to ordinary motion and color clues, flicker analysis is also carried out by analyzing the video in wavelet domain. These clues are combined to reach a final decision. © 2005 IEEE