Smoke detection method based on local extreme co-occurrence pattern and energy analysis

A local extremum and energy analysis technology, applied in the field of video processing, can solve problems such as effect degradation

Inactive Publication Date: 2018-11-30
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0006] The methods mentioned above are effective when the smoke concentration is relati...

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  • Smoke detection method based on local extreme co-occurrence pattern and energy analysis
  • Smoke detection method based on local extreme co-occurrence pattern and energy analysis

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Embodiment Construction

[0034] The present invention will be further described below in conjunction with drawings and embodiments.

[0035] Such as figure 1 Shown, the present invention is concretely realized as follows:

[0036] Moving object detection.

[0037] Common background modeling methods include Gaussian mixture model (GMM), codebook (Codebook) and ViBe method. GMM is the most popular parametric technique, which is adaptive and multimodal capable of handling dynamic environmental backgrounds. However, its sensitivity cannot be adjusted accurately; and its ability to successfully handle high and low frequencies in the background is controversial; moreover, parameter estimation of the model on images containing noise is problematic. The main purpose of the codebook is to obtain the time series model of each pixel, so that the time fluctuation problem can be solved well, but it consumes a lot of memory. The ViBe algorithm proposed by Barnich and VanDroogenbroeck is a background extraction ...

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Abstract

The invention provides a novel and robust video smoke detection method under a scene with thin smoke. The method is manly composed of three stages, namely a preprocessing stage, a feature extraction stage and an image classification stage. At the preprocessing stage, a background differential algorithm is used to extract a motion foreground region of video frames; then an HSV color space is adopted to act on the motion foreground region to recognize smoke pixels; then a local extreme co-occurrence pattern (LECoP) is used to calculate texture features, and smoke energy analysis is used to calculate energy features; and last, a feature vector training support vector machine (SVM) is used for smoke recognition. It can be seen from an experiment result that the method can effectively detect smoke.

Description

[0001] Yuan Mei technical field [0002] The invention belongs to the technical field of video processing, and in particular relates to a video smoke detection method based on local extremum co-occurrence mode and energy analysis. Background technique [0003] Fire is often accompanied by smoke and fire, so smoke or fire can be used as a detection element for effective early warning of fire to a certain extent. This paper mainly focuses on smoke detection. For the fire detection system, in the early days, fire detection was mainly carried out through smoke sensors, heat sensors, CO sensors, etc., but these methods have many shortcomings. Propagation time, which creates a time delay, which poses major difficulties for fire fighting; moreover, these methods are suitable for inspection indoors, if they are applied to a larger detection area, because the smoke travels in all directions For the sake of this, the performance is greatly degraded. Due to the limitations of traditi...

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Application Information

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IPC IPC(8): G06K9/62G06K9/00G06K9/32G06T7/215G06T7/45
CPCG06T7/215G06T7/45G06T2207/30232G06T2207/10016G06V20/40G06V20/52G06V10/255G06F18/2411G06F18/253G06F18/214
Inventor 袁梅黄俊全太锋胡煦
Owner CHONGQING UNIV OF POSTS & TELECOMM
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