Video smoke detection method based on mixed Gaussian model and morphological characteristics

Abstract

The invention discloses a video smoke detection method based on a mixed Gaussian model and morphological characteristics. The method comprises the following steps of: acquiring a foreground motion area by adopting a mixed Gaussian model algorithm aiming at a video image acquired by a supervisory camera of a fixed view field; then removing noise points by adopting morphological filtering; counting the morphological characteristics of the motion area, such as the diffusion velocity, the geometry, the ambiguity, the irregularity, and the like; and finally, carrying out a synthetic judgment on the counted characteristics of the motion area so as to judge whether smoke exists in the video image or not. The self-adaptation mixed Gaussian model (i.e. each pixel is modeled by utilizing mixed Gaussian distribution) can be used for reliably eliminating the influences of interferences of illumination changes, messy background motions, ripples, leaf wobbles, rain, snow, and the like. The invention realizes the quick and accurate identification on whether the video image has smoke or not through the synthetic judgment of the detected various morphological characteristics of the foreground motion area and also greatly reduces the false alarm rate.

Description

technical field [0001] The invention belongs to the field of pattern recognition, specifically relates to the technical field of fire monitoring, in particular to a video image pattern recognition method based on a mixed Gaussian model and morphological features to detect the initial phenomenon of fire—smoke. Background technique [0002] The prevention and detection of fire has always been the goal pursued by human beings in the process of fighting against fire. When a fire breaks out, the smoke is the first to appear, and it appears earlier than the flame, so the real-time monitoring of the smoke is of great significance for the early warning of the fire. For the indoor environment, smoke-sensing, temperature-sensing, and light-sensing detectors can be installed, which use the characteristics of smoke, temperature, and light of fire flames to detect fires. But for a long time, in tall spaces or outdoors, early fire smoke detection has been a difficult problem worldwide. ...

AI Technical Summary

Problems solved by technology

Some existing video smoke detection methods, either due to weak anti-interference ability, or because the discriminant features used are relatively simple, make it difficult to avoid false positives and false negatives, and their adaptability and stability in complex monitoring environments are also not good. satisfactory

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