国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

In recent years, frequent fire accidents have seriously affected the ecological environment and social economy,video surveillance system plays a very important role in fire prevention and environmental monitoring. To overcome the shortcomings of traditional video flame detection methods, such as manual extraction of flame features, low recognition rate and high false detection rate, a flame detection algorithm based on feature detection, multi-target tracking and deep learning is proposed. The algorithm extracts the dynamic object in video by the Gaussian mixture model motion detection method, then filters the suspected flame object through the color model combined with HSI and RGB, and tracks the extracted object with multi-target tracking algorithm. Finally, the stable target is judged by CaffeNet model and the result of flame discrimination is obtained. Experiments show that the algorithm can detect the video flame accurately, identify, and the average recognition accuracy of the flame video data set is 98.79%, which can supply the demand of real-time flame detection.