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A fast flame recognition algorithm based on tracking and marking

Fire will cause great loss to life, property and ecological environment. The key to reduce the loss of fire is to send out fire warning quickly. Based on the requirement of real-time flame recognition of embedded video monitoring equipment, this paper proposes a fast flame recognition algorithm based on tracking and marking. First, the improved color model of flame is used to complete the segmentation of candidate regions. Then neural network is used to give the final classification results. The core idea of the proposed algorithm is to replace the process of classification with the process of tracking and marking, for the reason that the time consuming of classification through neural network is much longer than that of tracking and marking and the number of calls of neural network can reduce in the proposed algorithm. Comparing the proposed algorithm with other flame recognition algorithms in recent years, it can not only keep the high accuracy of classification, but also shorten the execution time of the algorithm.