Smoke vehicle detection method based on self-organizing background difference model and multi-feature fusion

A technology of multi-feature fusion and detection method is applied in the field of black smoke vehicle detection based on self-organized background difference model and multi-feature fusion, which can solve the problems of low recognition rate and unsatisfactory effect.

Active Publication Date: 2019-01-11
SOUTHEAST UNIV
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Problems solved by technology

However, the current artificial intelligence implementation scheme has a low recognition rate and the effect is not ideal.

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  • Smoke vehicle detection method based on self-organizing background difference model and multi-feature fusion
  • Smoke vehicle detection method based on self-organizing background difference model and multi-feature fusion
  • Smoke vehicle detection method based on self-organizing background difference model and multi-feature fusion

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[0067] The technical solutions provided by the present invention will be described in detail below in conjunction with specific examples. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0068] The invention proposes a smoky car detection method based on self-organized background difference model and multi-feature fusion, which can automatically identify smoky cars by analyzing road monitoring video, which is of great significance to the control of smoky cars. The invention adopts the self-organized background difference model to detect the moving target, characterizes the characteristics of the vehicle through multi-feature fusion, and judges whether the current vehicle is a smoky vehicle by means of a pruned neural network classifier. The self-organized background difference model adopted in the present invention not only has strong robustness t...

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Abstract

The invention discloses a black smoke vehicle detection method based on a self-organizing background difference model and a multi-feature fusion, which comprises the following steps: detecting a moving object from a video surveillance by using the self-organizing background difference model, and determining a key area; transforming the key region image into YCrCb color space and extracting the color moment feature; transforming the key regions into gray space, and extracting the local ternary mode histogram and edge direction histogram; according to the position of the key region of the current frame, extracting the corresponding regions of several frames from the whole frame sequence, concatenating the same features extracted from all the temporal regions to form the feature vectors of each class, and normalizing the feature vectors of each class to form the final feature vectors; classifying the final eigenvectors by using the pruned radial basis function neural network classifier; identifying the key areas of smoke and further identifying the smoky vehicles. The invention can further improve the identification rate, reduce the false alarm rate, and has good identification effecton smoky vehicles with relatively light black smoke.

Description

technical field [0001] The invention belongs to the technical field of moving target detection in computer vision, and relates to a smoky car detection method based on a self-organized background difference model and multi-feature fusion. Background technique [0002] In recent years, more and more cities have suffered from smog. There are many factors that cause smog, among which exhaust emissions from motor vehicles using diesel engines are one of the main sources. Air pollution is harmful to human health, and the World Health Organization has confirmed and announced that the particulate matter emitted by diesel vehicles is a strong carcinogen. [0003] At this stage, the phenomenon of diesel vehicles emitting black smoke is still very serious, and it is commonplace during the stages of starting, accelerating, going uphill, and overloading. Some diesel vehicles pass through the city, which is like poisoning along the way. Taking Beijing as an example, the "Notice on Adop...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/246
CPCG06T7/246G06T2207/20081G06T2207/20084G06T2207/30236G06T2207/10016
Inventor 路小波陶焕杰
Owner SOUTHEAST UNIV
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