Fire detection algorithms for video images of large space structures
In large space structures, the latest fire detection methods are based on video image processing and data fusion. But the false positive rate and false negative rate remain unsatisfactory and need improving. The emphases of this paper are target extraction and recognition. A new adaptively updating target extraction algorithm (NAUTEA) is proposed by which the intact target can be extracted in time. In addition, some fire video image recognition algorithms, such as fuzzy neural network (FNN) and FGALSSVM (Fuzzy GALSSVM), are studied and improved. To verify the performance of these algorithms, a prototype system is developed, and a series of algorithm tests on a fire video are conducted. These tests make it clear that, the accurate, robust and real-time fire detection can be realized.
nautea fnn fgalssvm probability density algorithm dempstershafer (ds) historical data fusion