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Traditional fire detection methods are based on smoke and detectors. They are not suitable for high and large-span space structures because of their limited detection range. The latest fire detection methods are based on video-image processing and data fusion. However, false positive rate and false negative rate still remain unsatisfactory and need improvement. In this paper, some fire video-image detection algorithms are studied. A prototype system is developed to verify the performance of these algorithms. A series of algorithm tests on fire video file are conducted. It is found that detection algorithms on the basis of fuzzy neural network behave more fine than those based on probability density, historical data fusion can lower false positive rate and false negative rate remarkably, it is not true that evidence combination rules (Dempster-Shafer rules) can always get a more satisfying fusion result.