Fire and smoke detection in video with optimal mass transport based optical flow and neural networks.
ABSTRACT Detection of fire and smoke in video is of practical and theoretical interest. In this paper, we propose the use of optimal mass transport (OMT) optical flow as a low-dimensional descriptor of these complex processes. The detection process is posed as a supervised Bayesian classification problem with spatio-temporal neighborhoods of pixels;feature vectors are composed of OMT velocities and R,G,B color channels. The classifier is implemented as a single-hidden-layer neural network. Sample results show probability of pixels belonging to fire or smoke. In particular, the classifier successfully distinguishes between smoke and similarly colored white wall, as well as fire from a similarly colored background.
- SourceAvailable from: Behcet Töreyin
Article: Video fire detection – Review[Show abstract] [Hide abstract]
ABSTRACT: This is a review article describing the recent developments in Video based Fire Detection (VFD). Video surveillance cameras and computer vision methods are widely used in many security applications. It is also possible to use security cameras and special purpose infrared surveillance cameras for fire detection. This requires intelligent video processing techniques for detection and analysis of uncontrolled fire behavior. VFD may help reduce the detection time compared to the currently available sensors in both indoors and outdoors because cameras can monitor “volumes” and do not have transport delay that the traditional “point” sensors suffer from. It is possible to cover an area of 100 km2 using a single pan-tilt-zoom camera placed on a hilltop for wildfire detection. Another benefit of the VFD systems is that they can provide crucial information about the size and growth of the fire, direction of smoke propagation.Digital Signal Processing 01/2013; 23(6):1827–1843. · 1.92 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Video surveillance systems are often used to detect anomalies: rare events which demand a human response, such as a fire breaking out. Automated detection algorithms enable vastly more video data to be processed than would be possible otherwise. This note presents a video analytics framework for the detection of amorphous and unstructured anomalies such as fire, targets in deep turbulence, or objects behind a smoke-screen. Our approach uses an off-line supervised training phase together with an on-line Bayesian procedure: we form a prior, compute a likelihood function, and then update the posterior estimate. The prior consists of candidate image-regions generated by a weak classifier. Likelihood of a candidate region containing an object of interest at each time step is computed from the photometric observations coupled with an optimal-mass-transport optical-flow field. The posterior is sequentially updated by tracking image regions over time and space using active contours thus extracting samples from a properly aligned batch of images. The general theory is applied to the video-fire-detection problem with excellent detection performance across substantially varying scenarios which are not used for training.18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, September 11-14, 2011; 01/2011