Research On Wildfire Smoke Detection Technology Based On Motion Feature

Forest is an important resource which is essential to human being’s survival and development. As a major nature disaster, the forest fire damages large quantity of forest resources very year. In order to put out the forest fire as fast as possible and reduce the loss, it makes sense to detect the fire in time. Nowadays, video network has been established in most forests to monitor fire. However, it purely relies on artificial recognition. Forest fire video monitoring network based on hardware and advanced detection algorithm can not only automatically alarm of fire, but also decrease the cost significantly. Smoke is the most significant phenomenon prior to a fire, so research on video-based smoke recognition makes a good sense.In this paper, the author proposed a frame of forest fire smoke recognition system. Several major results of this study are as follows:(1) Gray projection algorithm which is one of electronic image stabilization methods is put forward to maintain the video series smooth and stable. The algorithm has good effect on image sequence with colorful and small timing jitter.(2) Moving target detection algorithm based on Gaussian statistical model is proposed. Scattered trivial regions are clustered by minimum distance clustering with threshold.(3)10dimension image features of forest fire smoke are extracted based on distinguishing visual characteristic of smoke. Multiple weak classifiers, classification and regression tree, is constructed with Adaboost algorithm. Strong classifier is established combined with those weak classifiers, and could be used to detect forest fire smoke. Simulation results show that the identification ratio is above94.7%, while false positive ratio was4.53%, false negative ratio was5.91%.(4) Integration of wireless networks, video monitoring and other techniques contributes to the intelligent forest fire smoke recognition system. This system implements the results of this study, including electronic image stabilization, forest fire video motion detection algorithm, image feature extraction method and Adaboost classification and recognition method. It was deployed in a forest in Beijing, and successfully captured a true forest fire and all simulated forest fires tests. The identification ratio was99.32%.