CN115035445A - Floater detection method and system based on background pixel value difference algorithm - Google Patents
Floater detection method and system based on background pixel value difference algorithm Download PDFInfo
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Abstract
The invention discloses a floater detection method and system based on a background pixel value difference algorithm, which are characterized in that a background pixel value difference algorithm is adopted to obtain pixel difference values of video frame image pixel points and background image pixel points, the pixel difference values are compared and judged with a preset threshold value to determine all foreground pixel points, floaters are outlined according to all the foreground pixel points, and therefore the method and system have better accuracy and real-time performance.
Description
Technical Field
The invention relates to the technical field of floater detection, in particular to a floater detection method and system based on a background pixel value difference algorithm.
Background
The most direct mode for improving the river pollution is to process floaters in the river channel, the water surface of the river channel is kept clean and tidy, in order to process the floaters in the river channel in time, the floaters in the river channel need to be detected in time, in the prior art, a frame difference method is generally adopted to identify and detect the floaters in the river channel, a target object is mainly judged through the difference between adjacent frame images in a gray scale space, the method is widely applied to scenes which are simple and have multiple static backgrounds, however, the scene with water streaks, such as the water surface, has high false detection rate in the scene with complex light shadows.
Disclosure of Invention
In view of this, the invention provides a method and a system for detecting a floating object based on a background pixel value difference algorithm, which can solve the defect of high false judgment rate in a dynamic scene in the existing floating object detection method.
The technical scheme of the invention is realized as follows:
a floater detection method based on a background pixel value difference algorithm specifically comprises the following steps:
step S1, constructing a background model;
step S2, acquiring a river channel sampling video, and acquiring video frame image pixel points according to the river channel sampling video;
step S3, acquiring corresponding background image pixel points in a background model according to the video frame image pixel points;
step S4, obtaining pixel difference values of video frame image pixel points and background image pixel points according to a background pixel value difference algorithm;
step S5, comparing and judging the pixel difference value with a preset threshold value, determining all foreground pixel points, and determining floaters according to the foreground pixel points;
step S6, updating the background model;
in step S7, the preset threshold is updated.
As a further alternative of the method for detecting a floating object based on the background pixel value difference algorithm, the step S1 specifically includes the following steps:
step S11, acquiring a background image, and acquiring N frames of images from the background image;
step S12, acquiring the pixel value of each pixel point in the N frames of images;
and step S13, constructing a background model according to the N frames of images and the pixel value of each pixel point.
As a further alternative of the method for detecting a floating object based on the background pixel value difference algorithm, the step S6 specifically includes the following steps:
step S61, determining all the pixels on the water surface according to the judgment result in the step S5;
step S62, acquiring pixel values of all the water surface pixel points;
and step S63, replacing the pixel values of the corresponding pixel points in the background model according to the pixel values of all the water surface pixel points, thereby realizing the updating of the background model.
As a further alternative of the method for detecting a floating object based on the background pixel value difference algorithm, the magnitude of the background model update frequency is determined according to the complexity of the background model, and the method specifically includes the following steps:
acquiring pixel difference values of video frame image pixel points and background image pixel points, and determining the complexity of a background model according to the pixel difference values;
and determining the updating frequency of the background model according to the complexity of the background model.
As a further alternative of the method for detecting a floating object based on the background pixel value difference algorithm, the preset threshold is determined according to the complexity of a background model, and specifically includes the following steps:
acquiring pixel difference values of video frame image pixel points and background image pixel points, and determining the complexity of a background model according to the pixel difference values;
and determining the size of a preset threshold according to the complexity of the background model.
A float detection system based on a background pixel value difference algorithm, the system comprising:
the building module is used for building a background model;
the first acquisition module is used for acquiring a river channel sampling video and acquiring video frame image pixel points according to the river channel sampling video;
the second acquisition module is used for acquiring corresponding background image pixel points in a background model according to the video frame image pixel points;
the computing module is used for obtaining pixel difference values of the video frame image pixel points and the background image pixel points according to a background pixel value difference algorithm;
the judging module is used for comparing and judging the pixel difference value with a preset threshold value, determining all foreground pixel points and determining floaters according to the foreground pixel points;
the first updating module is used for updating the background model;
and the second updating module is used for updating the preset threshold value.
As a further alternative to the background pixel value difference algorithm based float detection system, the construction module comprises:
the acquisition module is used for acquiring a background image and acquiring N frames of images from the background image;
the third acquisition module is used for acquiring the pixel value of each pixel point in the N frames of images;
and the first processing module is used for constructing a background model according to the N frames of images and the pixel value of each pixel point.
As a further alternative to the background pixel value difference algorithm based float detection system, the first update module comprises:
the fourth acquisition module is used for determining all the water surface pixel points according to the judgment result of the judgment module;
the fifth acquisition module is used for acquiring pixel values of all the water surface pixel points;
and the replacing module is used for replacing the pixel values of the corresponding pixel points in the background model according to the pixel values of all the water surface pixel points.
As a further alternative to the background pixel value difference algorithm based float detection system, the first update module further comprises an update frequency determination module, the update frequency determination module comprising:
the sixth acquisition module is used for acquiring pixel difference values of the video frame image pixel points and the background image pixel points and determining the complexity of the background model according to the pixel difference values;
and the second processing module is used for determining the updating frequency of the background model according to the complexity of the background model.
As a further alternative of the background pixel value difference algorithm-based float detection system, the second update module comprises:
the seventh obtaining module is used for obtaining the complexity of the background model;
and the third processing module is used for determining the size of a preset threshold according to the complexity of the background model.
The beneficial effects of the invention are: the method comprises the steps of obtaining pixel difference values of video frame image pixel points and background image pixel points by adopting a background pixel value difference algorithm, comparing and judging the pixel difference values with a preset threshold value, determining all foreground pixel points, outlining floaters according to all the foreground pixel points, and having better accuracy and real-time performance.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a floating object detection method based on a background pixel value difference algorithm according to the present invention;
fig. 2 is a schematic diagram of the floating object detection system based on the background pixel value difference algorithm according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, a method for detecting a floating object based on a background pixel value difference algorithm specifically includes the following steps:
step S1, constructing a background model;
step S2, acquiring a river channel sampling video, and acquiring video frame image pixel points according to the river channel sampling video;
step S3, acquiring corresponding background image pixel points in a background model according to the video frame image pixel points;
step S4, obtaining pixel difference values of video frame image pixel points and background image pixel points according to a background pixel value difference algorithm;
step S5, comparing and judging the pixel difference value with a preset threshold value, determining all foreground pixel points, and determining floaters according to the foreground pixel points;
step S6, updating the background model;
in step S7, the preset threshold is updated.
In this embodiment, a background pixel value difference algorithm is adopted to obtain pixel difference values of video frame image pixel points and background image pixel points, the pixel difference values are compared and judged with a preset threshold value to determine all foreground pixel points, and floaters are outlined according to all the foreground pixel points, so that the accuracy and the real-time performance are better.
It should be noted that, a sampled video is taken, a pixel point Xi in a frame image of the sampled video is taken, the pixel value of the pixel point Xi is i (Xi), the pixel value of a background model pixel point corresponding to the pixel point Xi is b (Xi), and a difference value between the pixel value i (Xi) of a current image pixel point and the pixel value b (Xi) of a corresponding background image pixel point is taken: d (xi) { I (x), B (xi) }, the difference D (xi) { I (x), B (xi) }, if less than the threshold R (xi), the condition is fulfilled. The number of all pixel points meeting the condition is recorded as M, and because the number of the pixel points close to the background in the current image is inevitably smaller than the minimum value of the number of the pixel points close to the background pixel value in the background image, the pixel points are indicated as foreground pixel points, and therefore, the number of the pixel points close to the background pixel value in the background image is set as W. And when M < W, the point Xi is the foreground pixel point.
Preferably, the step S1 specifically includes the following steps:
step S11, acquiring a background image, and acquiring N frames of images from the background image;
step S12, acquiring the pixel value of each pixel point in the N frames of images;
and step S13, constructing a background model according to the N frames of images and the pixel value of each pixel point.
In this embodiment, a background image is collected, an N-frame image is used to construct a background model, and in the collected N-frame image, pixel point information B (Xi) of each pixel point Xi is represented as B (Xi) ═ { B1(Xi), B2(Xi), … …, bn (Xi) }, where N is the collected N-frame image for constructing the background, and is generally between 100-.
Preferably, the step S6 specifically includes the following steps:
step S61, determining all the pixels on the water surface according to the judgment result in the step S5;
step S62, acquiring pixel values of all the water surface pixel points;
and step S63, replacing the pixel values of the corresponding pixels in the background model according to the pixel values of all the water surface pixels, thereby realizing the updating of the background model.
In this embodiment, because the water surface scene is in a dynamic change, the background model needs to be updated continuously to ensure the continuous accuracy of the algorithm, and the specific principle is as follows: and if a certain point Xb in the current video image is judged as the water surface background, replacing the corresponding point pixel value B (Xb) of a certain random frame in the corresponding background model with the pixel value of the point, and finishing one-time replacement, namely one-time updating.
Preferably, the magnitude of the background model update frequency is determined according to the complexity of the background model, and the method specifically includes the following steps:
acquiring pixel difference values of video frame image pixel points and background image pixel points, and determining the complexity of a background model according to the pixel difference values;
and determining the updating frequency of the background model according to the complexity of the background model.
In this embodiment, the key defining the update frequency is the complexity of the scene, that is, the complexity of the background model, and the complexity of the background model is measured by determining the difference between the pixel value i (xi) of the pixel point and the background pixel point b (xi), where the larger the difference is, the more complex the background model is, and therefore, the higher the complexity of the background model is, the higher the update frequency is; the lower the complexity of the background model, the less frequent the update.
Preferably, the preset threshold is determined according to the complexity of the background model, and specifically includes the following steps:
acquiring pixel difference values of video frame image pixel points and background image pixel points, and determining the complexity of a background model according to the pixel difference values;
and determining the size of a preset threshold according to the complexity of the background model.
In this embodiment, the preset threshold r (xi) of the foreground pixel needs to be dynamically adjusted according to the change of the complexity of the background model, when the complexity of the background model is high, the preset threshold needs to be increased appropriately to prevent the detection from being too sensitive to falsely detect the environmental noise such as light and shadow as the floating object, and when the water surface is calm and the environmental noise interference is small, the preset threshold r (xi) is decreased to ensure the detection sensitivity.
Preferably, a floating object detecting system based on a background pixel value difference algorithm, the system includes:
the building module is used for building a background model;
the first acquisition module is used for acquiring a river channel sampling video and acquiring video frame image pixel points according to the river channel sampling video;
the second acquisition module is used for acquiring corresponding background image pixel points in a background model according to the video frame image pixel points;
the computing module is used for obtaining pixel difference values of the video frame image pixel points and the background image pixel points according to a background pixel value difference algorithm;
the judging module is used for comparing and judging the pixel difference value with a preset threshold value, determining all foreground pixel points and determining floaters according to the foreground pixel points;
the first updating module is used for updating the background model;
and the second updating module is used for updating the preset threshold value.
In the embodiment, the pixel difference value between the video frame image pixel point and the background image pixel point is obtained by adopting a background pixel value difference algorithm, the pixel difference value is compared and judged with a preset threshold value to determine all foreground pixel points, and floaters are outlined according to all the foreground pixel points, so that the accuracy and the real-time performance are better.
Preferably, the building block comprises:
the acquisition module is used for acquiring a background image and acquiring N frames of images from the background image;
the third acquisition module is used for acquiring the pixel value of each pixel point in the N frames of images;
and the first processing module is used for constructing a background model according to the N frames of images and the pixel value of each pixel point.
Preferably, the first updating module includes:
the fourth acquisition module is used for determining all the water surface pixel points according to the judgment result of the judgment module;
the fifth acquisition module is used for acquiring pixel values of all the water surface pixel points;
and the replacing module is used for replacing the pixel values of the corresponding pixels in the background model according to the pixel values of all the water surface pixels.
In this embodiment, because the water surface scene is in a dynamic change, the background model needs to be updated continuously to ensure the continuous accuracy of the algorithm, and the specific principle is as follows: if a certain point Xb in the current video image is judged as the water surface background, replacing the corresponding point pixel value B (Xb) of a random certain frame in the corresponding background model by the pixel value of the point, and finishing one replacement, namely one updating.
Preferably, the first updating module further comprises an updating frequency determining module, and the updating frequency determining module comprises:
the sixth acquisition module is used for acquiring pixel difference values of the video frame image pixel points and the background image pixel points and determining the complexity of the background model according to the pixel difference values;
and the second processing module is used for determining the updating frequency of the background model according to the complexity of the background model.
In this embodiment, the key for defining the update frequency is the complexity of the scene, that is, the complexity of the background model, and the complexity of the background model is measured by determining the difference between a pixel value i (xi) and a background pixel value b (xi), where the larger the difference is, the more complex the background model is, and therefore, the higher the complexity of the background model is, the higher the update frequency is; the less complex the background model, the less frequent the update.
Preferably, the second updating module includes:
a seventh obtaining module, configured to obtain complexity of the background model;
and the third processing module is used for determining the size of a preset threshold according to the complexity of the background model.
In this embodiment, the preset threshold r (xi) of the foreground pixel needs to be dynamically adjusted according to the change of the complexity of the background model, when the complexity of the background model is high, the preset threshold needs to be increased appropriately to prevent the detection from being too sensitive to falsely detect the environmental noise such as light and shadow as the floating object, and when the water surface is calm and the environmental noise interference is small, the preset threshold r (xi) is decreased to ensure the detection sensitivity.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A floating object detection method based on a background pixel value difference algorithm is characterized by comprising the following steps:
step S1, constructing a background model;
step S2, acquiring a river channel sampling video, and acquiring video frame image pixel points according to the river channel sampling video;
step S3, acquiring corresponding background image pixel points in a background model according to the video frame image pixel points;
step S4, obtaining pixel difference values of the video frame image pixel points and the background image pixel points according to a background pixel value difference algorithm;
step S5, comparing and judging the pixel difference value with a preset threshold value, determining all foreground pixel points, and determining floaters according to the foreground pixel points;
step S6, updating the background model;
in step S7, the preset threshold is updated.
2. The method according to claim 1, wherein the step S1 specifically comprises the following steps:
step S11, acquiring a background image, and acquiring N frames of images from the background image;
step S12, acquiring the pixel value of each pixel point in the N frames of images;
and step S13, constructing a background model according to the N frames of images and the pixel value of each pixel point.
3. The method according to claim 2, wherein the step S6 specifically comprises the following steps:
step S61, determining all the pixels on the water surface according to the judgment result in the step S5;
step S62, acquiring pixel values of all the water surface pixel points;
and step S63, replacing the pixel values of the corresponding pixel points in the background model according to the pixel values of all the water surface pixel points, thereby realizing the updating of the background model.
4. The method according to claim 3, wherein the background model update frequency is determined according to the complexity of the background model, and the method specifically comprises the following steps:
acquiring pixel difference values of video frame image pixel points and background image pixel points, and determining the complexity of a background model according to the pixel difference values;
and determining the updating frequency of the background model according to the complexity of the background model.
5. The method according to claim 1, wherein the preset threshold is determined according to the complexity of a background model, and the method specifically comprises the following steps:
acquiring pixel difference values of video frame image pixel points and background image pixel points, and determining the complexity of a background model according to the pixel difference values;
and determining the size of a preset threshold according to the complexity of the background model.
6. A float detection system based on a background pixel value difference algorithm, the system comprising:
the building module is used for building a background model;
the first acquisition module is used for acquiring a river channel sampling video and acquiring video frame image pixel points according to the river channel sampling video;
the second acquisition module is used for acquiring corresponding background image pixel points in a background model according to the video frame image pixel points;
the computing module is used for obtaining pixel difference values of the video frame image pixel points and the background image pixel points according to a background pixel value difference algorithm;
the judging module is used for comparing and judging the pixel difference value with a preset threshold value, determining all foreground pixel points and determining floaters according to the foreground pixel points;
the first updating module is used for updating the background model;
and the second updating module is used for updating the preset threshold value.
7. A background pixel value difference algorithm based on a float detection system according to claim 6 wherein the construction module comprises:
the acquisition module is used for acquiring a background image and acquiring N frames of images from the background image;
the third acquisition module is used for acquiring the pixel value of each pixel point in the N frames of images;
and the first processing module is used for constructing a background model according to the N frames of images and the pixel value of each pixel point.
8. A background pixel value difference algorithm based float detection system according to claim 7 wherein said first update module comprises:
the fourth acquisition module is used for determining all the water surface pixel points according to the judgment result of the judgment module;
the fifth acquisition module is used for acquiring pixel values of all the water surface pixel points;
and the replacing module is used for replacing the pixel values of the corresponding pixel points in the background model according to the pixel values of all the water surface pixel points.
9. A background pixel value difference algorithm based float detection system according to claim 8 wherein said first update module further comprises an update frequency determination module comprising:
the sixth acquisition module is used for acquiring pixel difference values of the video frame image pixel points and the background image pixel points and determining the complexity of the background model according to the pixel difference values;
and the second processing module is used for determining the updating frequency of the background model according to the complexity of the background model.
10. A background pixel value difference algorithm based on a float detection system according to claim 6 wherein said second update module comprises:
the seventh obtaining module is used for obtaining the complexity of the background model;
and the third processing module is used for determining the size of a preset threshold according to the complexity of the background model.
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| CN116679080A (en) * | 2023-05-30 | 2023-09-01 | 广州伏羲智能科技有限公司 | River surface flow velocity determining method and device and electronic equipment |
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