Conference Paper

Fire and smoke detection in video with optimal mass transport based optical flow and neural networks.

DOI: 10.1109/ICIP.2010.5652119 In proceeding of: Proceedings of the International Conference on Image Processing, ICIP 2010, September 26-29, Hong Kong, China
Source: DBLP

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.

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