Video image fuzzy anomaly detection method based on machine learning

A video image and machine learning technology, applied in the field of intelligent transportation, can solve the problems of aging equipment, easy omission or negligence, time-consuming and labor-intensive, etc., and achieve the effect of strong robustness

Inactive Publication Date: 2016-12-07
QINGDAO UNIV
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Problems solved by technology

[0002] Since there are a large number of surveillance cameras in the intelligent transportation field of each city (more than 1,000 in each city), it is time-consuming and labor-intensive to rely on manual methods to troubleshoot cameras with fuzzy problems, and it is very prone to omissions or negligence, which makes the cameras that should be maintained If it is not detected, the camera will often blur due to focusing problems or equipment aging. This kind of blurring is usually not easy to detect, but it will bring disastrous consequences for later analysis of video surveillance or use of surveillance video to restore the scene

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  • Video image fuzzy anomaly detection method based on machine learning
  • Video image fuzzy anomaly detection method based on machine learning
  • Video image fuzzy anomaly detection method based on machine learning

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Embodiment Construction

[0013] refer to figure 1 and figure 2 , the present embodiment adopts the following technical solutions:

[0014] 1. First, the high-definition video color image is converted to a grayscale image. When converted to grayscale, the grayscale value is the average of the pixel values ​​of the three channels to balance the requirements of each channel for detection accuracy.

[0015] 2. In video surveillance video images, clear images account for the majority and blurred images account for a minority, so 6,000 blurred images are collected as positive samples, and 3,000 clear images are used as negative samples. and non-blurred artificial classification into two categories, compute gradient histogram features for each image. The traditional histogram of gradient (HOG) algorithm is used to estimate the motion of pedestrians and achieves better recognition results. The HOG algorithm is a descriptor that is sensitive to changes in the orientation of the image space. By using the gr...

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Abstract

The invention discloses a video image fuzzy anomaly detection method based on machine learning and belongs to the technical field of intelligent traffic. The method comprises the following steps of: firstly converting high-definition video color images into grayscale images; manually classifying the converted grayscale images into two categories according to fuzziness and non-fuzziness and calculating the gradient histogram feature of each image; using the gradient histogram features as the classification features, training the classification features by using the support vector machine, and saving the trained parameters; and using the trained parameters to calculate the gradient histogram feature of newly input images and then to calculate the output result of the support vector machine; and judging the image as fuzzy image if the support vector machine is positive or judging the image as a non-fuzzy image if the support vector machine is negative. The video image fuzzy anomaly detection method based on machine learning has strong robustness and can be applied to determining whether the video image has fuzzy problems or not.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a method for detecting blurred anomalies of video images based on machine learning. Background technique: [0002] Since there are a large number of surveillance cameras in the intelligent transportation field of each city (more than 1,000 in each city), it is time-consuming and labor-intensive to rely on manual methods to troubleshoot cameras with fuzzy problems, and it is very prone to omissions or negligence, which makes the cameras that should be maintained If it is not detected, the camera will often blur due to focusing problems or equipment aging. This kind of blurring is usually not easy to detect, but it will bring disastrous consequences for later analysis of video surveillance or use of surveillance video to restore the scene . Contents of the invention [0003] In view of the above problems, the technical problem to be solved by the present inven...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2411G06F18/214
Inventor 王国栋
Owner QINGDAO UNIV
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