A Video-Based Forest Fire Detection Method

Abstract

The present invention provides a forest fire detection method based on video. Based on the unique growth and change characteristics of forest fire smoke, forest fire smoke detection is realized by discriminating and analyzing the growth and change of alternative smoke areas, so that smoke detection is not easily affected by other methods. The interference of objects with similar colors and shapes can also effectively eliminate the influence of other forms of moving objects, and has high detection robustness; at the same time, the forest fire early video detection method of the present invention uses the accumulation area, and within a certain frame number interval Multiple comparisons based on the cumulative area make the discriminant analysis of regional growth changes more stable; the technology of the present invention can effectively solve the automatic fire detection of remote monitoring video through the detection of smoke when forest fires occur in the early stage and alarm issues.

Description

technical field [0001] The invention belongs to the fields of forest fire prevention and video target detection, and in particular relates to a video-based forest fire detection method. Background technique [0002] Traditional fire detection and alarm technology usually utilizes sensors capable of sensing smoke particles. However, these sensors only work when they are close to fireworks, and the working distance is very limited. In addition, due to the fast air flow in open field conditions, it is difficult for smoke particles to be effectively received by the sensors. The video-based fire detection method uses the deployed remote monitoring cameras to monitor whether a fire occurs within a relatively long distance around. For large-scale forest or mountain forest areas, it is generally necessary to set up watchtowers at a certain distance and deploy monitoring cameras to cover the entire area, and transmit all real-time acquired videos to the monitoring center. It takes ...

AI Technical Summary

Problems solved by technology

In addition, the method of segmenting potential smoke areas based on motion features may also fail for long-distance smoke, because long-distance smoke generally moves slowly in the video, and it is difficult to effectively and accurately obtain smoke areas through motion information

In addition, machine learning and training techniques are also widely used in video detection of fire smoke. Such techniques require large-scale training samples, but in practice, people can obtain very limited forest fire video data of various scenes; for Long-distance video smog generally lacks significant features such as texture and shape for machine learning technology to learn and train due to unclear imaging and small smoke areas, which also makes the final detection effect unsatisfactory.

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