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Black-smoke vehicle detection method based on multi-scale block LBP and hidden Markov model

A Hidden Markov, detection method technology, applied in character and pattern recognition, instruments, computer parts, etc., to save costs, improve detection efficiency, and reduce false alarm rates

Active Publication Date: 2018-12-07
SOUTHEAST UNIV
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a smoky car detection method based on multi-scale block LBP and hidden Markov model, which can make up for the low efficiency of traditional manual monitoring of smoky cars and fully describe the key areas Dynamic features to reduce false positive rate

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  • Black-smoke vehicle detection method based on multi-scale block LBP and hidden Markov model
  • Black-smoke vehicle detection method based on multi-scale block LBP and hidden Markov model
  • Black-smoke vehicle detection method based on multi-scale block LBP and hidden Markov model

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Embodiment Construction

[0048] The detailed process of the inventive method will be clearly and completely described below in conjunction with the accompanying drawings and embodiments of the specification.

[0049] The present invention provides a smoky car detection method based on multi-scale block LBP and hidden Markov model, the flow chart of which is as follows figure 1 As shown, follow the steps below:

[0050] Step 1: Detect moving targets and determine key areas from surveillance video;

[0051] Step 2: Extract multi-scale block LBP features, increase scale and position information;

[0052] Step 3: Use the hidden Markov model to describe the dynamic characteristics of key areas, and divide the current frame into smoky frames and non-smoky frames;

[0053] Step 4: Through the analysis of the video sequence, combined with the distribution characteristics of the black smoke frame, further identify the black smoke car.

[0054] The method for detecting the moving target in the step 1 adopts ...

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Abstract

The invention relates to a black-smoke vehicle detection method based on a multi-scale block LBP and a hidden Markov model. The method includes: (1), detecting a moving target from a monitoring videoand determining a key region; (2), extracting multi-scale block LBP features increasing scale and position information; (3) depicting dynamic features of the key region by using a hidden Markov modeland dividing current frames into black smoke frames and non-black smoke frames; and (4), carrying out analysis on a video sequence and identifying a black smoke vehicle further by combining a distribution characteristic of the black smoke frames. Therefore, automatic detection of black smoke vehicles is realized and the detection efficiency is improved; and because the dynamic features of the keyregion are depicted by using the hidden Markov model, the false alarm rate is reduced.

Description

technical field [0001] The invention belongs to the technical field of image processing and firework detection, in particular to a black smoke vehicle detection method based on multi-scale block LBP and hidden Markov model. Background technique [0002] Smoky vehicles are a kind of highly polluting vehicles. The black smoke exhaust not only pollutes the air, but also damages human health. [0003] At present, most of the detection of smoky vehicles still relies on manual road inspection, which is time-consuming, labor-intensive, affects traffic, and is inefficient. The other way is the traditional manual video monitoring method. This method is to set up a camera above the road and then manually play and view each video. Confirm the license plate of the smoky car in this video. According to the survey, cities that adopt this method need to employ a large number of workers, and at least one person must be assigned to view each channel of video. After a long period of viewing...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46
CPCG06V20/40G06V20/54G06V10/40G06V10/467G06V2201/08
Inventor 路小波陶焕杰
Owner SOUTHEAST UNIV
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