CN105430267A - Method for adaptively adjusting camera parameters based on face image illumination parameters - Google Patents
Method for adaptively adjusting camera parameters based on face image illumination parameters Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/61—Control of cameras or camera modules based on recognised objects
- H04N23/611—Control of cameras or camera modules based on recognised objects where the recognised objects include parts of the human body
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/10—Cameras or camera modules comprising electronic image sensors; Control thereof for generating image signals from different wavelengths
- H04N23/12—Cameras or camera modules comprising electronic image sensors; Control thereof for generating image signals from different wavelengths with one sensor only
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/67—Focus control based on electronic image sensor signals
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/70—Circuitry for compensating brightness variation in the scene
- H04N23/73—Circuitry for compensating brightness variation in the scene by influencing the exposure time
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Abstract
The invention discloses a method for adaptively adjusting camera parameters based on face image illumination parameters. The method comprises the following steps: 10, acquiring a face image: analyzing a face image under the current scenario from a video frame acquired by a camera; 20, processing the face image: calculating a brightness parameter, a color parameter and an ambiguity parameter of a face area by using a face detection algorithm; and 30, adjusting camera parameters, comprising the following steps: 31, adjusting the shutter speed or gain of the camera according to the brightness parameter; 32, adjusting the gamma parameter of the camera according to the color parameter; and 33, adjusting the focusing parameter of the camera according to the ambiguity parameter. The method solves the problem of automatically adjusting camera parameters in the complex scenario, and improves the quality of the face image acquired by the camera in the complex scenario.
Description
Technical field
The present invention relates to image communication technology field, particularly a kind of camera parameters self-adapting regulation method based on facial image illumination parameter.
Background technology
In most video monitoring scene, how the normally most important image information of facial image, therefore obtain the key that high-quality facial image is current camera parameter adaptive adjustment technology.Present Domestic is outer, and to adjust the Measures compare of camera parameters according to facial image few, but the method directly adjusting the parameter of video camera according to whole picture quality is a lot.The mode being adjusted camera parameters by picture quality is mainly comprised:
1, image luminance information is utilized automatically to adjust the method for camera gain and shutter speed: video camera enters bright size by what adjust that different shutter speeds controls transducer, and the multiplication factor of sensor signal is controlled by adjustment gain size, in different illumination brightness scenes, the brightness that video camera needs the size by adjusting shutter speed and gain to control image scene remains on suitable scope, concrete steps are: the first brightness mean information of computed image, then judge that whether brightness average is in suitable scope, if not in suitable scope, then preferentially adjust brightness average by adjustment shutter parameter, because too low shutter speed can cause motion image blurring, when adjusting shutter parameter, need to keep shutter speed to be greater than 1/25 second, when shutter speed was at 1/25 second, picture does not still reach suitable brightness, then need to improve picture brightness by adjustment gain parameter.Although the method can obtain good effect in the scene that whole picture brightness is more balanced, but poor effect in wide dynamic picture, particularly in the scene of recognition of face, if face background luminance is higher, the method can adjust camera parameters automatically, reduce the brightness of whole picture, make the facial image that collects excessively dark, affect the effect of recognition of face; If brightness ratio is lower, the method can adjust camera parameter automatically, improves the brightness of whole picture, makes the facial image that collects excessively bright, affects the effect of recognition of face.
2, image blur is utilized automatically to adjust the method for the focusing parameter of video camera: first obtain figure sharpness parameter, the information of the HFS of image is more, then the marginal information of key diagram picture is more, acutance is higher; Otherwise, the information of image medium-high frequency part is fewer, illustrate that image sharpness is lower, fuzzyyer, then high-frequency signal judges image fog-level in the accounting of whole image information is calculated, pass through while the focusing parameter of adjustment video camera again, while calculate the accounting of high-frequency signal, adjustment always reaches the peak value of high-frequency signal accounting, now camera acquisition to image be exactly focus on after optimized image.Although the method can obtain good effect in shallow depth of field picture, but poor effect in the picture of the high depth of field, special in the scene of recognition of face, if the whole picture depth of field is higher, specific focusing parameter can not cover whole picture completely, camera focus parameter in the method can self-adaptative adjustment to the highest situation of whole picture sharpness.Now, if the distant view texture more complicated in picture, then Focus Club is at distant view place, and close shot picture can be more empty; If close shot picture more complicated, then focus goes out at close shot, and distant view is more empty, and therefore the method cannot ensure to obtain facial image clearly in the face recognition application scene in the high depth of field.
Summary of the invention
The present invention, for solving the problem, provides a kind of camera parameters self-adapting regulation method based on facial image illumination parameter, solves the problem of adjustment camera parameters automatically in complex scene, improves the quality that video camera obtains facial image in complex scene.
For achieving the above object, the technical solution used in the present invention is:
A kind of camera parameters self-adapting regulation method based on facial image illumination parameter of the present invention's announcement as described in Figure 1, comprises the following steps:
10. facial image obtains: from the frame of video of camera acquisition, parse the facial image under current scene;
20. face image processings: utilize the luminance parameter of Face datection algorithm calculating human face region, color parameter and ambiguity parameter;
30. camera parameters adjustment, comprise the following steps:
31. according to luminance parameter adjustment camera shutter speed or the gain adjusting video camera;
32. adjust the gamma parameter of video camera according to color parameter;
33. according to the focusing parameter of ambiguity parameter adjustment video camera.
In described step 20, luminance parameter is brightness value, and its acquisition methods is: facial image is converted to gray level image, calculates brightness value to gray level image;
Described color parameter comprises H histogram and V histogram, and its acquisition methods is: facial image is transferred to HSV color space, then calculates H histogram and the V histogram of this facial image;
Described ambiguity parameter is high-frequency information accounting, its acquisition methods is: facial image is carried out Fourier transform, obtain the spectrum information of facial image, the spectrum information of latter half is high-frequency information, calculates the ratio that facial image medium-high frequency information accounts for all information of image.
Described step 31 is specially: when brightness value is lower than luminance threshold, reduces the shutter speed of video camera or improves the gain of video camera; When brightness value is higher than threshold value, improves the shutter speed of video camera or reduce the gain of video camera.
Described luminance threshold is 164, and it obtains from the training data study based on face standard picture.
Described step 32 is specially: the H histogram calculated from facial image and V histogram are compared with standard H histogram and standard V histogram respectively, modulation gamma parameter until H histogram and V histogram with standard H histogram and standard V histogram difference minimum.
Described standard H histogram and the histogrammic acquisition methods of standard V are: prepare at least 5000 standard certificate photo facial images as sample image, extract the tone passage H and saturation passage V that often open in the HSV color space of sample image, and calculate the histogram average of all sample image H passages and V passage, using the standard H histogram of the equal value histogram of H passage as facial image, using the standard V histogram of the equal value histogram of V passage as facial image.
Described step 33 is specially: constantly the focusing parameter of adjustment video camera is until the high-frequency information accounting of facial image reaches peak value.
Also comprise video camera coarse adjustment before described step 33: when video camera installation testing, adjustment camera focus is until obtain face clearly.
The invention has the beneficial effects as follows: the present invention adjusts camera shutter speed and gain size automatically according to image brightness parameter respectively, automatically adjust the gamma parameter of video camera according to color parameter and automatically adjust the focusing parameter of video camera according to ambiguity parameter, the over-exposed video camera caused of scene hypograph solving face backlight or strong frontlighting cannot adjust the problem of picture brightness according to area-of-interest, avoid because camera has aberration and picture complexity to cause the image blurring situation collected, by automatically regulating camera parameters, video camera is made to be adjusted to the most applicable current scene shooting state, thus the color of face picture is got a new look, definition obtains and improves.
Accompanying drawing explanation
Fig. 1 is general flow chart of the present invention;
Fig. 2 contrasts according to the front and back of the shutter speed of face brightness adjustment video camera and gain size;
Fig. 3 adjusts the front and back contrast of the gamma parameter of video camera automatically according to the color parameter of facial image;
Fig. 4 adjusts the front and back contrast of camera focus parameter automatically according to the high-frequency information accounting of facial image.
Embodiment
In order to make technical problem to be solved by this invention, technical scheme and beneficial effect clearly, understand, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.
A kind of camera parameters self-adapting regulation method based on facial image illumination parameter that the present invention discloses, it comprises the following steps:
10. facial image obtains: from the frame of video of camera acquisition, parse the facial image under current scene;
20. face image processings: utilize the luminance parameter of Face datection algorithm calculating human face region, color parameter and ambiguity parameter;
30. camera parameters adjustment, comprise the following steps:
31. according to luminance parameter adjustment camera shutter speed or the gain adjusting video camera;
32. adjust the gamma parameter of video camera according to color parameter;
33. according to the focusing parameter of ambiguity parameter adjustment video camera.
In described step 20, luminance parameter is brightness value, and its acquisition methods is: facial image is converted to gray level image, calculates brightness value to gray level image;
Described color parameter comprises H histogram and V histogram, and its acquisition methods is: facial image is transferred to HSV color space, then calculates H histogram and the V histogram of this facial image;
Described ambiguity parameter is high-frequency information accounting, its acquisition methods is: facial image is carried out Fourier transform, obtain the spectrum information of facial image, the spectrum information of latter half is high-frequency information, calculates the ratio that facial image medium-high frequency information accounts for all information of image.
In the present embodiment, step 31 is specially: when brightness value is lower than luminance threshold, reduces the shutter speed of video camera or improves the gain of video camera; When brightness value is higher than threshold value, improves the shutter speed of video camera or reduce the gain of video camera.Wherein luminance threshold is 164, and it obtains from the training data study based on face standard picture.This method chooses the face standard picture of big data quantity usually as sample, adopt the treatment step that step 10-step 30 is same, carry out learning training, the luminance standard value obtained is luminance threshold, as shown in Figure 2, for the front and back facial image design sketch of gain of camera shutter speed or adjustment video camera, after adjustment, image brightness obviously promotes.
In the present embodiment, step 32 is specially: the H histogram calculated from facial image and V histogram are compared with standard H histogram and standard V histogram respectively, modulation video camera gamma parameter until H histogram and V histogram with standard H histogram and standard V histogram difference minimum.As shown in Figure 3, be facial image design sketch before and after adjustment video camera gamma, the color stable of image after adjustment, closer to very color.
Described standard H histogram and the histogrammic acquisition methods of standard V are: prepare at least 5000 standard certificate photo facial images as sample image, extract the tone passage H and saturation passage V that often open in the HSV color space of sample image, and calculate the histogram average of all sample image H passages and V passage, using the standard H histogram of the equal value histogram of H passage as facial image, using the standard V histogram of the equal value histogram of V passage as facial image.
Described step 33 is specially: constantly the focusing parameter of adjustment video camera is until the high-frequency information accounting of facial image reaches peak value., wherein the computing formula of high-frequency information accounting HFR is as follows:
Focusing parameter adjustment detailed process is: end user's face detection algorithm obtains face position in the picture, calculate the high-frequency information accounting of facial image, use the focus knob of motor adjustment camera lens, adjust to always high-frequency information accounting the highest time, now the focal position of video camera is on face, and face obtains the highest definition.The computing formula of high-frequency information accounting is as follows:
。In formula, A
kfor the amplitude of high-frequency information, A
0represent DC component, n represents spectrum component sequence number, and spectrum information is arranged to high frequency by low frequency.
As shown in Figure 4, be facial image design sketch before and after adjustment focusing parameter, after adjustment, facial image is more clear.
In order to the adjustment of step 33 pair focusing parameter of being more convenient for, also comprise video camera coarse adjustment: when video camera installation testing before described step 33, adjustment camera focus is until obtain face clearly.
The present invention adjusts camera shutter speed and gain size automatically according to image brightness parameter respectively, automatically adjust the gamma parameter of video camera according to color parameter and automatically adjust the focusing parameter of video camera according to ambiguity parameter, the over-exposed video camera caused of scene hypograph solving face backlight or strong frontlighting cannot adjust the problem of picture brightness according to area-of-interest, with solve, avoid because of the camera image blurring situation collected that has aberration to cause, this technical scheme can regulate camera parameter automatically, the color of face picture is got a new look, definition obtains and improves.
Above-mentioned explanation illustrate and describes the preferred embodiments of the present invention, be to be understood that the present invention is not limited to the form disclosed by this paper, should not regard the eliminating to other embodiments as, and can be used for other combinations various, amendment and environment, and can in invention contemplated scope herein, changed by the technology of above-mentioned instruction or association area or knowledge.And the change that those skilled in the art carry out and change do not depart from the spirit and scope of the present invention, then all should in the protection range of claims of the present invention.
Claims (7)
1., based on a camera parameters self-adapting regulation method for facial image illumination parameter, it is characterized in that, comprise the following steps:
10. facial image obtains: from the frame of video of camera acquisition, parse the facial image under current scene;
20. face image processings: utilize the luminance parameter of Face datection algorithm calculating human face region, color parameter and ambiguity parameter;
30. camera parameters adjustment, comprise the following steps:
31. according to luminance parameter adjustment camera shutter speed or the gain adjusting video camera;
32. adjust the gamma parameter of video camera according to color parameter;
33. according to the focusing parameter of ambiguity parameter adjustment video camera.
2. a kind of camera parameters self-adapting regulation method based on facial image illumination parameter as claimed in claim 1, is characterized in that:
In described step 20, luminance parameter is brightness value, and its acquisition methods is: facial image is converted to gray level image, calculates brightness value to gray level image;
Described color parameter comprises H histogram and V histogram, and its acquisition methods is: facial image is transferred to HSV color space, then calculates H histogram and the V histogram of this facial image;
Described ambiguity parameter is high-frequency information accounting, its acquisition methods is: facial image is carried out Fourier transform, obtain the spectrum information of facial image, the spectrum information of latter half is high-frequency information, calculates the ratio that facial image medium-high frequency information accounts for all information of image.
3. a kind of camera parameters self-adapting regulation method based on facial image illumination parameter as claimed in claim 2, it is characterized in that: described step 31 is specially: when brightness value is lower than luminance threshold, reduce the shutter speed of video camera or improve the gain of video camera; When brightness value is higher than threshold value, improves the shutter speed of video camera or reduce the gain of video camera.
4. a kind of camera parameters self-adapting regulation method based on facial image illumination parameter as claimed in claim 3, is characterized in that: described luminance threshold is 164, and it obtains from the training data study based on face standard picture.
5. a kind of camera parameters self-adapting regulation method based on facial image illumination parameter as claimed in claim 2, it is characterized in that: described step 32 is specially: the H histogram calculated from facial image and V histogram are compared with standard H histogram and standard V histogram respectively, modulation gamma parameter until H histogram and V histogram with standard H histogram and standard V histogram difference minimum.
6. a kind of camera parameters self-adapting regulation method based on facial image illumination parameter as claimed in claim 5, it is characterized in that: described standard H histogram and the histogrammic acquisition methods of standard V are: prepare at least 5000 standard certificate photo facial images as sample image, extract the tone passage H and saturation passage V that often open in the HSV color space of sample image, and calculate the histogram average of all sample image H passages and V passage, using the standard H histogram of the equal value histogram of H passage as facial image, using the standard V histogram of the equal value histogram of V passage as facial image.
7. a kind of camera parameters self-adapting regulation method based on facial image illumination parameter as claimed in claim 2, is characterized in that: described step 33 is specially: constantly the focusing parameter of adjustment video camera is until the high-frequency information accounting of facial image reaches peak value.
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