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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Cai, B., Xiong, L., Zhao, J. (2019). Wild Flame Detection Using Weight Adaptive Particle Filter from Monocular Video. In: Panigrahi, B., Trivedi, M., Mishra, K., Tiwari, S., Singh, P. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 670. Springer, Singapore. https://doi.org/10.1007/978-981-10-8971-8_33
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DOI: https://doi.org/10.1007/978-981-10-8971-8_33
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