Objective  Video surveillance is increasingly applied to the early detection of forest fire smoke. The existing video forest fire smoke detection methods are mostly based on pixel extraction of smoke characteristics for analysis and detection, but when the smoke is early or the smoke is far from the camera, the smoke only appears in a small area on the video image. Moreover, the diffusion of smoke is irregular, and the background environment is complex and changeable, resulting in insignificant pixel-based features, which makes it more difficult to automatically detect pixel-based smoke. Based on the principle of visible light video image processing, this paper proposes a forest fire video smoke detection method based on local area image dynamic characteristics to improve the accuracy and sensitivity of forest fire video smoke detection.

  Method  The video images were selected as the research object. One frame per second was taken to generate an image sequence, and the image sequences were divided into multiple levels and different scales; using the principle of image signal-to-noise ratio, we calculated the signal-to-noise ratio of continuous image sequences after blocking; the adaptive threshold was obtained according to the signal-to-noise ratio of the background image, and the image block whose brightness changes in the image sequence to be detected was determined to be the suspected smoke block; the LBP texture feature of the suspected smoke block was extracted, and the support vector machine was used to distinguish the smoke area.

  Result  Using the value component of the HSV color space, smoke areas can be effectively extracted. The videos with forest fire smoke were selected to verify the proposed smoke change detection method. The analysis results showed that the method can determine the image block where the smoke occurred and excluded some non-smoke interference factors.

  Conclusion  This paper proposes a video forest fire smoke detection technology based on brightness characteristics and LBP texture features of local area, which can accurately locate the smoke occurrence area and exclude some interference factors. The average detection recognition rate reaches more than 92%, which is helpful for real-time forest fire smoke automatic detection and improving the detection rate of forest fire smoke. It has a strong practicality.