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Erschienen in:

2019 | OriginalPaper | Buchkapitel

Wild Flame Detection Using Weight Adaptive Particle Filter from Monocular Video

verfasst von : Bo Cai, Lu Xiong, Jianhui Zhao

Erschienen in: Smart Innovations in Communication and Computational Sciences

Verlag: Springer Singapore

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Abstract

Wild flame detection from monocular video is an important step for monitoring of fire disaster. Flame region is complex and keeps varying, thus difficult to be tracked automatically. A weight adaptive particle filter algorithm is proposed in this paper to obtain flame detection with higher accuracy. The particle filter method considers color feature model, edge feature model, and texture feature model and then fuses them into a multi-feature model. During which related adaptive weighting parameters are defined and used for the features. For each particle corresponding to target region being tracked, the proportion of fire pixels in the area is computed with Gaussian mixture model, and then it is used as an additional adaptive parameter for the related particle. The presented algorithm has been tested with real video clips, and experimental results have proved the efficiency of the novel detection method.

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Vorheriges Kapitel Matching Algorithm and Parallax Extraction Based on Binocular Stereo Vision

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Titel: Wild Flame Detection Using Weight Adaptive Particle Filter from Monocular Video
verfasst von: Bo Cai
Lu Xiong
Jianhui Zhao
Verlag: Springer Singapore
Buch: Smart Innovations in Communication and Computational Sciences
Print ISBN: 978-981-10-8970-1

Electronic ISBN: 978-981-10-8971-8

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-981-10-8971-8_33

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