CN113158877A - Imaging deviation analysis and biopsy method, imaging deviation analysis and biopsy device, and computer storage medium - Google Patents

Imaging deviation analysis and biopsy method, imaging deviation analysis and biopsy device, and computer storage medium Download PDF

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CN113158877A
CN113158877A CN202110414614.4A CN202110414614A CN113158877A CN 113158877 A CN113158877 A CN 113158877A CN 202110414614 A CN202110414614 A CN 202110414614A CN 113158877 A CN113158877 A CN 113158877A
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imaging
result
binocular camera
test object
imaging result
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马家康
谢旭明
刘欢
王行骏
吕炯炯
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Shanghai Yuncong Enterprise Development Co ltd
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    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/40Spoof detection, e.g. liveness detection
    • G06V40/45Detection of the body part being alive
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
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    • G06V10/25Determination of region of interest [ROI] or a volume of interest [VOI]

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Abstract

本申请提供一种成像偏差分析及活体检测方法、装置及计算机存储介质,主要包括提供双目相机拍摄测试对象,以确定双目相机的成像距离,并获取双目相机对应于成像距离的第一成像结果和第二成像结果,识别第一成像结果和第二成像结果中的测试对象,获得测试对象在第一成像结果中的第一位置信息和在第二成像结果中的第二位置信息,并根据第一位置信息和第二位置信息,获得双目相机对应于成像距离的成像偏差分析结果。据此,本申请可针对双目相机对应于不同成像距离的成像偏差执行动态测算,并可提高双目相机的检测结果的准确性。

Figure 202110414614

The present application provides an imaging deviation analysis and living body detection method, device, and computer storage medium, which mainly include providing a binocular camera to shoot a test object, to determine the imaging distance of the binocular camera, and obtaining the first binocular camera corresponding to the imaging distance. the imaging result and the second imaging result, identifying the test object in the first imaging result and the second imaging result, obtaining the first position information of the test object in the first imaging result and the second position information in the second imaging result, And according to the first position information and the second position information, the imaging deviation analysis result of the binocular camera corresponding to the imaging distance is obtained. Accordingly, the present application can perform dynamic measurement for the imaging deviation of the binocular camera corresponding to different imaging distances, and can improve the accuracy of the detection result of the binocular camera.

Figure 202110414614

Description

Imaging deviation analysis and biopsy method, imaging deviation analysis and biopsy device, and computer storage medium
Technical Field
The embodiment of the application relates to the technical field of picture identification, in particular to an imaging deviation analysis and living body detection method, an imaging deviation analysis and living body detection device and a computer storage medium.
Background
At present, the technical bottleneck of the binocular camera-based biopsy is mainly caused by the fact that imaging deviation exists between a color camera and an infrared camera, that is, imaging deviation exists between the color camera and the infrared camera, which can cause the situation of misrecognition of the binocular camera to increase, particularly when the distance between a photo of a person in a warehouse and another person not in the warehouse is not far, the situation that the person not in the warehouse passes through the recognition often occurs, and the technical hole has serious security threat.
In view of this, how to overcome the problem of high misrecognition rate of the binocular camera due to the imaging deviation between the color camera and the infrared camera is a technical subject to be solved by the present application.
Disclosure of Invention
In view of the foregoing, the present application provides an imaging deviation analysis and biopsy method, an imaging deviation analysis and biopsy device, and a computer storage medium, which can accurately analyze an imaging deviation value of a binocular camera and improve accuracy of a detection result of the binocular camera.
The first aspect of the present application provides an imaging deviation analysis method, which includes providing a binocular camera to photograph a test object to determine an imaging distance of the binocular camera, and acquiring a first imaging result and a second imaging result of the binocular camera corresponding to the imaging distance; identifying the test object in the first imaging result and the second imaging result, and obtaining first position information of the test object in the first imaging result and second position information of the test object in the second imaging result; and obtaining an imaging deviation analysis result of the binocular camera corresponding to the imaging distance according to the first position information and the second position information.
A second aspect of the present application provides a computer storage medium having stored therein instructions for performing the steps of the imaging bias analysis method of the first aspect.
A third aspect of the present application provides a method for detecting a living body, which includes obtaining an imaging deviation analysis result of a binocular camera corresponding to an imaging distance by using the imaging deviation analysis method of the first aspect described above according to the imaging distance of the binocular camera, and calibrating the binocular camera according to the imaging deviation analysis result; and shooting a target object meeting the imaging distance by using the calibrated binocular camera to obtain an imaging result of the target object, and identifying the target object according to the imaging result.
A fourth aspect of the present application provides a computer storage medium having stored therein instructions for performing the steps of the living body detection method of the third aspect.
The present application provides in a fifth aspect an imaging bias analysis device, comprising: the imaging module is used for providing a binocular camera to shoot a test object so as to determine the imaging distance of the binocular camera, and acquiring a first imaging result and a second imaging result of the binocular camera corresponding to the imaging distance; the identification module is used for identifying the test object in the first imaging result and the second imaging result and obtaining first position information of the test object in the first imaging result and second position information of the test object in the second imaging result; and the analysis module is used for obtaining an imaging deviation analysis result of the binocular camera corresponding to the imaging distance according to the first position information and the second position information.
The sixth aspect of the present application provides a living body detection apparatus, comprising: a calibration module, configured to obtain an imaging deviation analysis result of the binocular camera corresponding to the imaging distance according to an imaging distance of the binocular camera and using the imaging deviation analysis apparatus according to the fifth aspect, and calibrate the binocular camera according to the imaging deviation analysis result; the detection module is used for shooting a target object meeting the imaging distance by using the calibrated binocular camera, obtaining an imaging result of the target object and identifying the target object according to the imaging result.
In summary, the binocular camera is used for performing dynamic measurement and calculation aiming at the imaging deviation of the binocular camera corresponding to different imaging distances by comparing the position information of the test object in the first imaging result and the second imaging result of the binocular camera, and the method and the device have the advantage of high accuracy in measurement and calculation of the imaging deviation.
Moreover, this application can further calibrate to binocular camera based on binocular camera's formation of image deviation analysis result, according to this, can improve the rate of accuracy of binocular camera's detection recognition result.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be 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 described in the embodiments of the present application, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a schematic flow chart of an imaging deviation analysis method according to a first embodiment of the present application.
Fig. 2 is a flowchart illustrating an imaging deviation analysis method according to a second embodiment of the present application.
Fig. 3 is a flowchart illustrating a living body detecting method according to a fourth embodiment of the present application.
Fig. 4 is a schematic structural diagram of an imaging deviation analysis apparatus according to a sixth embodiment of the present application.
FIG. 5 is a schematic diagram illustrating an architecture of a biopsy device according to a seventh embodiment of the present application.
Element number
40: a binocular camera; 400: an imaging deviation analyzing device; 402: an imaging module; 404: an identification module; 406: an analysis module; 500: a living body detecting device; 502: a calibration module; 504: and a detection module.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application shall fall within the scope of the protection of the embodiments in the present application.
As described in the background section, the present binocular camera has a problem that the misrecognition rate of the binocular camera is increased due to the imaging deviation between the color camera and the infrared camera. In view of the above, embodiments of the present disclosure provide an imaging offset analysis technique, which can perform dynamic measurement and calculation on imaging offsets of a binocular camera corresponding to different imaging distances and perform calibration on the binocular camera based on the measurement and calculation result of the imaging offsets, so as to improve accuracy of detection results of the binocular camera, and the imaging offset analysis and living body detection method, apparatus, and computer storage medium of the present disclosure will be described in detail below with reference to the accompanying drawings.
First embodiment
Fig. 1 shows a schematic flow chart of an imaging bias analysis method according to a first embodiment of the present application. As shown in the figure, the imaging deviation analysis method of the present embodiment mainly includes the following steps:
and S102, providing a binocular camera to shoot the test object so as to determine the imaging distance of the binocular camera, and acquiring a first imaging result and a second imaging result of the binocular camera corresponding to the imaging distance.
In this embodiment, the binocular camera includes a color camera and an infrared camera, the first imaging result of the binocular camera is a visible light image, and the second imaging result is an infrared image.
Alternatively, the size parameter and/or the position parameter of the region of interest of the binocular camera may be adjusted based on the imaging distance so that the test object may fall into the respective regions of interest of the first imaging result and the second imaging result of the binocular camera.
Step S104, identifying the test object in the first imaging result and the second imaging result, and obtaining first position information of the test object in the first imaging result and second position information in the second imaging result.
Alternatively, the first position information of the test object in the first imaging result may be obtained by identifying a region of interest in the first imaging result, and the first position information of the test object in the second imaging result may be obtained by identifying a region of interest in the second imaging result.
Alternatively, the first and second position information of the test object may be obtained by identifying the coordinate position of at least one feature point of the test object in the first imaging result (region of interest) and the second imaging result (region of interest), respectively.
For example, the first and second position information of the test object may be obtained by identifying coordinate positions of at least one vertex of the test object in the first and second imaging results, respectively.
Alternatively, the first position information and the second position information of the test object may be obtained by identifying imaging regions of the test object as a whole in the first imaging result and the second imaging result, respectively.
In this embodiment, the imaging area includes coordinate information of each feature point of the test object and size information of the test object.
For example, the imaging area of the test object in the first imaging result/the second imaging result may be determined by identifying coordinate information of respective vertices of the test object.
For another example, the imaging area of the test object in the first imaging result/the second imaging result may be determined by identifying coordinate information of one feature vertex of the test object and identifying width and height information of the test object.
And step S106, obtaining an imaging deviation analysis result of the binocular camera corresponding to the imaging distance according to the first position information and the second position information.
In this embodiment, the difference calculation may be performed according to the first position information and the second position information to obtain the imaging deviation value of the binocular camera corresponding to the imaging distance.
For example, when the first position information and the second position information are constituted by coordinate positions of at least one feature point of the test object in the first imaging result and the second imaging result, respectively, a difference value calculation may be performed for the coordinate positions of the same feature point of the test object in the two imaging results to obtain an imaging deviation analysis result of the binocular camera corresponding to the imaging distance.
For another example, when the first position information and the second position information are constituted by imaging regions of the test object as a whole in the first imaging result and the second imaging result, respectively, a difference value calculation may be performed for the imaging regions of the test object in the two imaging results to obtain an imaging deviation analysis result of the binocular camera corresponding to the imaging distance.
As can be seen from the above, the imaging deviation analysis method provided in this embodiment can dynamically measure and calculate the imaging deviation of the binocular camera at different imaging distances, so as to analyze whether the binocular camera is abnormal, thereby improving the accuracy of the subsequent detection result of the binocular camera.
Second embodiment
Fig. 2 shows a schematic flow chart of an imaging bias analysis method according to a second embodiment of the present application. As shown in the figure, the imaging deviation analysis method of the present embodiment mainly includes the following steps:
in step S202, a test object having a target area and a peripheral area surrounding the target area is acquired.
In this embodiment, the target area of the test object may be rectangular and have a first color, and the peripheral area of the test object may have a second color.
In this embodiment, the first color may be a reverse color of the second color, that is, the target area is a reverse color of the peripheral area.
Preferably, the first color (i.e., the target area) may be black, and the second color (i.e., the peripheral area) may be white.
Step S204, adjusting the distance between the binocular camera and the test object so that the target area of the test object completely falls into the respective interested areas of the first imaging result and the second imaging result of the binocular camera, and the peripheral area of the test object completely covers the respective interested areas of the first imaging result and the second imaging result so as to determine the imaging distance of the binocular camera.
Optionally, the size parameters and/or position parameters of the region of interest of the binocular camera may be adjusted based on the desired imaging distance to bring the final determined imaging distance into line with expectations.
And step S206, shooting the test object based on the determined imaging distance by the binocular camera, and acquiring a first imaging result and a second imaging result of the binocular camera corresponding to the imaging distance.
In this embodiment, the binocular camera includes a color camera and an infrared camera, the first imaging result is a visible light image, and the second imaging result is an infrared image.
Step S208, identifying the target area in the first imaging result and the second imaging result, and obtaining first position information of the target area in the first imaging result and second position information in the second imaging result.
Optionally, a coordinate position of at least one vertex of the target region of the test object in the first and second imaging results, respectively, may be identified to obtain first and second position information of the test object.
For example, the coordinate positions of the top left corner vertex of the target region in the first imaging result and the second imaging result, respectively, may be identified to obtain the first position information and the second position information.
Optionally, imaging regions of the target area in the first and second imaging results, respectively, may be identified to obtain the first and second position information.
In this embodiment, the imaging region of the target region may include each vertex coordinate information, length information, and width information of the target region.
For example, the imaging area of the target area in the first imaging result/the second imaging result may be determined by identifying coordinate information of each vertex of the target area (e.g., rectangle).
As another example, the imaging area of the target area in the first imaging result/the second imaging result may be determined by identifying coordinate information of one vertex (e.g., a top left vertex of the target area) of the target area (e.g., a rectangle), and identifying length information and width information of the target area.
Optionally, before executing this step, preprocessing may also be executed for the first imaging result and the second imaging result based on a preset preprocessing rule.
In this embodiment, the pre-processing performed on the first imaging result and the second imaging result may include at least one of a picture mirror rotation process, a picture pixel conversion process, and a picture compression process, so as to improve the accuracy of the subsequent imaging deviation analysis.
And step S210, obtaining an imaging deviation value of the binocular camera corresponding to the imaging distance according to the first position information and the second position information.
In this embodiment, a difference calculation may be performed on the first position information and the second position information of the target area to obtain an imaging deviation value of the binocular camera corresponding to the current imaging distance.
In step S212, it is determined whether an imaging deviation value of the binocular camera corresponding to the imaging distance is smaller than a preset deviation threshold, if so, step S214 is performed, and if not, step S216 is performed.
Alternatively, a preset deviation threshold may be set based on the resolutions of the first and second imaging results.
In this embodiment, when the resolution of the first imaging result and the second imaging result is 640 × 480, the preset deviation threshold may be set to 15 pixels.
In step S214, if the imaging deviation value of the binocular camera corresponding to the current imaging distance is analyzed to be smaller than the preset deviation threshold, an analysis result that the imaging deviation value of the binocular camera corresponding to the imaging distance is normal is output.
In step S216, if the imaging deviation value of the binocular camera corresponding to the current imaging distance is not less than the preset deviation threshold, outputting an analysis result that the imaging deviation value of the binocular camera corresponding to the imaging distance is abnormal.
In summary, in the imaging deviation analysis method of the embodiment, the peripheral area having the target area and surrounding the target area is used as the test object to dynamically measure and calculate the imaging deviation values of the binocular camera corresponding to different imaging distances, so that the accuracy of the imaging deviation measurement result can be improved.
Third embodiment
A third embodiment of the present application provides a computer storage medium having stored therein instructions for executing the steps of the imaging bias analysis method according to the first or second embodiment.
Fourth embodiment
Fig. 3 shows a schematic flow chart of a living body detection method according to a fourth embodiment of the present application. The living body detection method of the embodiment is suitable for various application scenes such as gate inhibition, gate and the like which need to execute identity authentication.
As shown in the figure, the living body detection method of the present embodiment mainly includes the following:
step S302, according to the imaging distance of the binocular camera, an imaging deviation analysis result of the binocular camera corresponding to the imaging distance is obtained by an imaging deviation analysis method, and the binocular camera is calibrated according to the imaging deviation analysis result.
In this embodiment, the imaging distance of the binocular camera may be determined according to the separation distance between the installation position of the binocular camera and the target detection position.
In this embodiment, the imaging deviation analysis method according to the first embodiment or the second embodiment may be used to obtain the imaging deviation value of the binocular camera corresponding to the imaging distance, and perform calibration on the binocular camera based on the imaging deviation value. For example, the imaging bias values may be set directly in the binocular camera for calibration.
And step S304, shooting the target object meeting the imaging distance by using the calibrated binocular camera, obtaining the imaging result of the target object, and identifying the target object according to the imaging result.
In the present embodiment, a binocular camera may be used to perform photographing on a target object located at a target detection position to obtain an imaging result that the target object satisfies an imaging distance.
Alternatively, it is possible to identify whether or not the target object in the imaging result matches a preset standard object.
In this embodiment, each standard imaging information corresponding to each preset standard object may be collected in advance, and the imaging result obtained by the binocular camera is compared with each standard imaging information, so as to obtain an identification result that the target object is matched or not matched with the preset standard object, for example, whether the target object is a person pre-registered in the bottom library of the identification terminal device.
Alternatively, the standard imaging information of the preset standard object may include facial feature information and/or posture feature information of the preset standard object.
In this embodiment, when the target object in the recognition imaging result matches the preset standard object, further performing live body detection on the target object (i.e., verifying whether the target object is a real live body) by using the infrared camera in the binocular camera.
In summary, the in-vivo detection method according to the embodiment of the present application performs detection and identification of the target object by using the binocular camera calibrated based on the imaging deviation analysis result, and can effectively improve the accuracy of the identification result of the binocular camera.
Fifth embodiment
A fifth embodiment of the present application provides a computer storage medium having stored therein instructions for executing the steps of the living body detecting method according to the fourth embodiment.
Sixth embodiment
Fig. 4 is a schematic structural diagram showing an imaging deviation analyzing apparatus according to a sixth embodiment of the present application. As shown in the drawing, the imaging deviation analysis apparatus 400 of the present embodiment mainly includes: an imaging module 402, a recognition module 404, and an analysis module 406.
The imaging module 402 is configured to provide a binocular camera 40 to shoot a test object, to determine an imaging distance of the binocular camera 40, and to acquire a first imaging result and a second imaging result of the binocular camera 40 corresponding to the imaging distance.
Optionally, the binocular camera 40 includes a color camera and an infrared camera, the first imaging result is a visible light image, and the second imaging result is an infrared image.
Optionally, the imaging module 402 is further configured to adjust a size parameter and/or a position parameter of the region of interest of the binocular camera 40 based on the imaging distance.
Optionally, the imaging module 402 is further configured to provide the binocular camera 40 to shoot the test object so that the test object falls into the region of interest of each of the first imaging result and the second imaging result of the binocular camera 40.
Optionally, the test object has a target area and a peripheral area surrounding the target area, wherein the target area is rectangular and has a first color, the peripheral area has a second color, the first color is a reverse color of the second color, preferably, the first color is black, and the second color is white; the imaging module 402 is further configured to provide the binocular camera 40 to photograph the test object, so that the target area of the test object completely falls within the respective regions of interest of the first and second imaging results of the binocular camera 40, and the peripheral area of the test object completely covers the respective regions of interest of the first and second imaging results.
The identification module 404 is configured to identify the test object in the first imaging result and the second imaging result, and obtain first position information of the test object in the first imaging result and second position information of the test object in the second imaging result.
Optionally, the identification module 404 is further configured to identify the region of interest in the first imaging result, and obtain the first position information of the test object in the first imaging result; and identifying the region of interest in the second imaging result, and obtaining the first position information of the test object in the second imaging result.
Optionally, the identification module 404 is further configured to identify a coordinate position of at least one feature point of the test object in the first imaging result and the second imaging result, respectively, so as to obtain the first position information and the second position information of the test object; or identifying imaging areas of the whole test object in the first imaging result and the second imaging result respectively to obtain the first position information and the second position information of the test object; the imaging area includes coordinate information of each feature point of the test object and size information of the test object.
Optionally, the identification module 404 is further configured to identify a coordinate position of at least one vertex of the target region in the first imaging result and the second imaging result, respectively, so as to obtain the first position information and the second position information of the test object; or identifying imaging areas of the target area in the first imaging result and the second imaging result respectively to obtain the first position information and the second position information of the test object; wherein the imaging region includes vertex coordinate information, length information, and width information of the target region.
Optionally, the identification module 404 is further configured to perform preprocessing on the first imaging result and the second imaging result based on a preset preprocessing rule; wherein the preset preprocessing rule comprises: at least one of a picture mirror rotation process, a picture pixel conversion process, and a picture compression process.
The analysis module 406 is configured to obtain an imaging deviation analysis result of the binocular camera 40 corresponding to the imaging distance according to the first position information and the second position information.
Optionally, the analysis module 406 is further configured to perform a difference calculation according to the first position information and the second position information, so as to obtain an imaging deviation value of the binocular camera 40 corresponding to the imaging distance.
Optionally, the analysis module 406 is further configured to, according to the imaging deviation value of the binocular camera 40 corresponding to the imaging distance and a preset deviation threshold, output an analysis result that the imaging deviation value of the binocular camera 40 corresponding to the imaging distance is normal if the imaging deviation value is smaller than the preset deviation threshold, and output an analysis result that the imaging deviation value of the binocular camera 40 corresponding to the imaging distance is abnormal if the imaging deviation value is not smaller than the preset deviation threshold.
Optionally, the analysis module 406 is further configured to set the preset deviation threshold based on the resolution of the first imaging result and the second imaging result.
Optionally, the preset deviation threshold comprises 15 pixels corresponding to a resolution of 640 x 480.
Seventh embodiment
Fig. 5 shows a schematic configuration diagram of a living body detecting apparatus according to a seventh embodiment of the present application. As shown in the figure, the living body detecting device 500 of the present embodiment mainly includes a calibration module 502 and a detection module 504.
The calibration module 502 is configured to obtain an imaging deviation analysis result of the binocular camera 40 corresponding to the imaging distance by using the imaging deviation analysis apparatus 400 according to the imaging distance of the binocular camera 40, and calibrate the binocular camera 40 according to the imaging deviation analysis result.
The detection module 504 is configured to capture a target object meeting the imaging distance by using the calibrated binocular camera 40, obtain an imaging result of the target object, and identify the target object according to the imaging result.
Optionally, the detection module 504 is further configured to, if it is identified that the target object in the imaging result matches a preset standard object, further perform living body detection on the target object by using an infrared camera in the binocular camera 40.
Optionally, the detection module 504 is further configured to acquire each standard imaging information corresponding to each preset standard object; and comparing the imaging result with each standard imaging information respectively to obtain the identification result of the target object matched with or not matched with the preset standard object.
Optionally, the standard imaging information includes facial feature information and/or posture feature information of the preset standard object.
In summary, the imaging deviation analysis method, the imaging deviation analysis device and the computer storage medium according to the embodiments of the present application can dynamically measure and calculate the imaging deviation of the binocular camera corresponding to different imaging distances.
In addition, the accuracy of the imaging deviation analysis result can be improved by setting the test object to have a target area and a peripheral area surrounding the target area, wherein the target area is rectangular and has a first color, the peripheral area has a second color, and the first color is a reverse color of the second color.
Furthermore, the in-vivo detection method, the in-vivo detection device and the computer storage medium provided by the application can improve the accuracy of the detection result of the binocular camera by calibrating the binocular camera according to the imaging deviation analysis result.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the embodiments of the present application, and are not limited thereto; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (19)

1.一种成像偏差分析方法,其特征在于,包括:1. an imaging deviation analysis method, is characterized in that, comprises: 提供双目相机拍摄测试对象,以确定所述双目相机的成像距离,并获取所述双目相机对应于所述成像距离的第一成像结果和第二成像结果;providing a binocular camera to photograph the test object, to determine the imaging distance of the binocular camera, and obtaining a first imaging result and a second imaging result of the binocular camera corresponding to the imaging distance; 识别所述第一成像结果和所述第二成像结果中的所述测试对象,获得所述测试对象在所述第一成像结果中的第一位置信息和在所述第二成像结果中的第二位置信息;以及Identify the test object in the first imaging result and the second imaging result, and obtain first position information of the test object in the first imaging result and the first position information in the second imaging result 2. location information; and 根据所述第一位置信息和所述第二位置信息,获得所述双目相机对应于所述成像距离的成像偏差分析结果。According to the first position information and the second position information, an imaging deviation analysis result of the binocular camera corresponding to the imaging distance is obtained. 2.根据权利要求1所述的成像偏差分析方法,其特征在于,所述双目相机包括彩色相机和红外相机,所述第一成像结果为可见光图像,所述第二成像结果为红外图像。2 . The imaging deviation analysis method according to claim 1 , wherein the binocular camera comprises a color camera and an infrared camera, the first imaging result is a visible light image, and the second imaging result is an infrared image. 3 . 3.根据权利要求1所述的成像偏差分析方法,其特征在于,所述方法还包括:3. The imaging deviation analysis method according to claim 1, wherein the method further comprises: 基于所述成像距离,调整所述双目相机的感兴趣区域的尺寸参数和/或位置参数。Based on the imaging distance, the size parameter and/or the position parameter of the region of interest of the binocular camera is adjusted. 4.根据权利要求3所述的成像偏差分析方法,其特征在于,4. The imaging deviation analysis method according to claim 3, wherein, 所述提供双目相机拍摄测试对象进一步包括:The providing the binocular camera to shoot the test object further includes: 提供所述双目相机拍摄所述测试对象,以使所述测试对象落入所述双目相机的所述第一成像结果和所述第二成像结果各自的所述感兴趣区域中;providing the binocular camera to photograph the test object, so that the test object falls into the respective regions of interest of the first imaging result and the second imaging result of the binocular camera; 所述识别所述第一成像结果和所述第二成像结果中的所述测试对象,获得所述测试对象在所述第一成像结果中的第一位置信息和在所述第二成像结果中的第二位置信息进一步包括:Identifying the test object in the first imaging result and the second imaging result, obtaining first position information of the test object in the first imaging result and in the second imaging result The second location information further includes: 识别所述第一成像结果中的所述感兴趣区域,获得所述测试对象在所述第一成像结果中的所述第一位置信息;并识别所述第二成像结果中的所述感兴趣区域,获得所述测试对象在所述第二成像结果中的所述第一位置信息。Identifying the region of interest in the first imaging result, obtaining the first position information of the test object in the first imaging result; and identifying the region of interest in the second imaging result area, and obtain the first position information of the test object in the second imaging result. 5.根据权利要求4所述的成像偏差分析方法,其特征在于,所述识别所述第一成像结果和所述第二成像结果中的所述测试对象包括:5. The imaging deviation analysis method according to claim 4, wherein the identifying the test object in the first imaging result and the second imaging result comprises: 识别所述测试对象的至少一个特征点分别在所述第一成像结果和所述第二成像结果中的坐标位置,以获得所述测试对象的所述第一位置信息和所述第二位置信息;或者Identifying the coordinate positions of at least one feature point of the test object in the first imaging result and the second imaging result, respectively, to obtain the first position information and the second position information of the test object ;or 识别所述测试对象整体分别在所述第一成像结果和所述第二成像结果中的成像区域,以获得所述测试对象的所述第一位置信息和所述第二位置信息;其中,所述成像区域包括所述测试对象的各所述特征点的坐标信息以及所述测试对象的尺寸信息。Identify the imaging regions of the test object as a whole in the first imaging result and the second imaging result, respectively, to obtain the first position information and the second position information of the test object; wherein, the The imaging area includes coordinate information of each feature point of the test object and size information of the test object. 6.根据权利要求5所述的成像偏差分析方法,其特征在于,所述根据所述第一位置信息和所述第二位置信息,获得所述双目相机对应于所述成像距离的成像偏差分析结果包括:6 . The imaging deviation analysis method according to claim 5 , wherein the imaging deviation of the binocular camera corresponding to the imaging distance is obtained according to the first position information and the second position information. 7 . Analysis results include: 根据所述第一位置信息和所述第二位置信息执行差值计算,获得所述双目相机对应于所述成像距离的成像偏差值。A difference value calculation is performed according to the first position information and the second position information, and an imaging deviation value of the binocular camera corresponding to the imaging distance is obtained. 7.根据权利要求6所述的成像偏差分析方法,其特征在于,所述方法还包括:7. The imaging deviation analysis method according to claim 6, wherein the method further comprises: 根据所述双目相机对应于所述成像距离的所述成像偏差值和预设偏差阈值,若所述成像偏差值小于所述预设偏差阈值,输出所述双目相机对应于所述成像距离的所述成像偏差值为正常的分析结果,若所述成像偏差值不小于所述预设偏差阈值,输出所述双目相机对应于所述成像距离的所述成像偏差值为异常的分析结果。According to the imaging deviation value of the binocular camera corresponding to the imaging distance and a preset deviation threshold, if the imaging deviation value is smaller than the preset deviation threshold, output the binocular camera corresponding to the imaging distance If the imaging deviation value of the binocular camera is not less than the preset deviation threshold, the imaging deviation value of the binocular camera corresponding to the imaging distance is an abnormal analysis result. . 8.根据权利要求7所述的成像偏差分析方法,其特征在于,所述方法包括:8. The imaging deviation analysis method according to claim 7, wherein the method comprises: 基于所述第一成像结果和所述第二成像结果的分辨率,设置所述预设偏差阈值;其中,The preset deviation threshold is set based on the resolution of the first imaging result and the second imaging result; wherein, 所述预设偏差阈值包括对应于640*480的分辨率的15个像素。The preset deviation threshold includes 15 pixels corresponding to a resolution of 640*480. 9.根据权利要求3所述的成像偏差分析方法,其特征在于,所述测试对象具有目标区域以及包围所述目标区域的外围区域,其中,所述目标区域呈矩形并具有第一颜色,所述外围区域具有第二颜色,所述第一颜色为所述第二颜色的反色,较佳地,所述第一颜色为黑色,所述第二颜色为白色。9 . The imaging deviation analysis method according to claim 3 , wherein the test object has a target area and a peripheral area surrounding the target area, wherein the target area is rectangular and has a first color, 9 . The peripheral area has a second color, and the first color is an inverse color of the second color. Preferably, the first color is black and the second color is white. 10.根据权利要求9所述的成像偏差方法,其特征在于,所述提供双目相机拍摄测试对象进一步包括:10. The imaging deviation method according to claim 9, wherein the providing the binocular camera to photograph the test object further comprises: 提供所述双目相机拍摄所述测试对象,以使所述测试对象的所述目标区域完全落入所述双目相机的所述第一成像结果和所述第二成像结果各自的所述感兴趣区域内,且所述测试对象的所述外围区域完全覆盖所述第一成像结果和所述第二成像结果各自的所述感兴趣区域。The binocular camera is provided to photograph the test object, so that the target area of the test object completely falls into the respective senses of the first imaging result and the second imaging result of the binocular camera. and the peripheral region of the test object completely covers the respective regions of interest of the first imaging result and the second imaging result. 11.根据权利要求10所述的成像偏差方法,其特征在于,所述识别所述第一成像结果和所述第二成像结果中的所述测试对象,获得所述测试对象在所述第一成像结果中的第一位置信息和在所述第二成像结果的第二位置信息包括:11 . The imaging deviation method according to claim 10 , wherein, by identifying the test object in the first imaging result and the second imaging result, obtaining the test object in the first imaging result. 12 . The first position information in the imaging result and the second position information in the second imaging result include: 识别所述目标区域的至少一个顶点分别在所述第一成像结果和所述第二成像结果中的坐标位置,以获得所述测试对象的所述第一位置信息和所述第二位置信息;或者Identifying the coordinate positions of at least one vertex of the target area in the first imaging result and the second imaging result, respectively, to obtain the first position information and the second position information of the test object; or 识别所述目标区域分别在所述第一成像结果和所述第二成像结果中的成像区域,以获得所述测试对象的所述第一位置信息和所述第二位置信息;其中,所述成像区域包括所述目标区域的各顶点坐标信息、长度信息和宽度信息。Identify the imaging regions of the target region in the first imaging result and the second imaging result, respectively, to obtain the first position information and the second position information of the test object; wherein, the The imaging area includes each vertex coordinate information, length information and width information of the target area. 12.根据权利要求1所述的成像偏差分析方法,其特征在于,在所述识别所述第一成像结果和所述第二成像结果中的所述测试对象的步骤之前,所述方法还包括:12 . The imaging deviation analysis method according to claim 1 , wherein, before the step of identifying the test object in the first imaging result and the second imaging result, the method further comprises: 12 . : 基于预设预处理规则,针对所述第一成像结果和第二成像结果执行预处理;performing preprocessing on the first imaging result and the second imaging result based on a preset preprocessing rule; 其中,所述预设预处理规则包括:图片镜像旋转处理,图片像素转换处理、图片压缩处理中的至少一个。Wherein, the preset preprocessing rule includes at least one of image mirror rotation processing, image pixel conversion processing, and image compression processing. 13.一种活体检测方法,其特征在于,所述方法包括:13. A method for detecting a living body, wherein the method comprises: 根据双目相机的成像距离,利用根据权利要求1至12中任一项所述的成像偏差分析方法,获得所述双目相机对应于所述成像距离的成像偏差分析结果,并根据所述成像偏差分析结果校准所述双目相机;以及According to the imaging distance of the binocular camera, the imaging deviation analysis method according to any one of claims 1 to 12 is used to obtain the imaging deviation analysis result of the binocular camera corresponding to the imaging distance, and according to the imaging distance the bias analysis result calibrates the binocular camera; and 利用校准后的所述双目相机针对满足所述成像距离的目标对象进行拍摄,获得所述目标对象的成像结果,并根据所述成像结果识别所述目标对象。The calibrated binocular camera is used to photograph a target object that meets the imaging distance, an imaging result of the target object is obtained, and the target object is identified according to the imaging result. 14.根据权利要求13所述的活体检测方法,其特征在于,所述方法还包括:14. The living body detection method according to claim 13, wherein the method further comprises: 若识别所述成像结果中的所述目标对象与预设标准对象匹配时,进一步利用所述双目相机中的红外相机针对所述目标对象执行活体检测。If it is recognized that the target object in the imaging result matches a preset standard object, the infrared camera in the binocular camera is further used to perform living detection on the target object. 15.根据权利要求14所述的活体检测方法,其特征在于,所述方法还包括:15. The living body detection method according to claim 14, wherein the method further comprises: 采集各所述预设标准对象对应的各标准成像信息;collecting each standard imaging information corresponding to each of the preset standard objects; 将所述成像结果分别与各所述标准成像信息进行比对,获得所述目标对象与所述预设标准对象匹配或不匹配的识别结果。The imaging results are compared with each of the standard imaging information respectively to obtain a recognition result that the target object matches or does not match the preset standard object. 16.根据权利要求15所述的活体检测方法,其特征在于,所述标准成像信息包括所述预设标准对象的面部特征信息和/或体态特征信息。16 . The living body detection method according to claim 15 , wherein the standard imaging information includes facial feature information and/or body feature information of the preset standard object. 17 . 17.一种计算机存储介质,其特征在于,所述计算机存储介质中储存有用于执行根据权利要求1至12中任一项所述的成像偏差分析方法的各所述步骤的指令,或者所述计算机存储介质中储存有用于执行根据权利要求13至16中任一项所述的活体检测方法的各所述步骤的指令。17. A computer storage medium, characterized in that the computer storage medium stores therein instructions for executing each of the steps of the imaging deviation analysis method according to any one of claims 1 to 12, or the The computer storage medium stores instructions for performing each of the steps of the method of living body detection according to any one of claims 13 to 16. 18.一种成像偏差分析装置,其特征在于,包括:18. An imaging deviation analysis device, comprising: 成像模块,用于提供双目相机拍摄测试对象,以确定所述双目相机的成像距离,并获取所述双目相机对应于所述成像距离的第一成像结果和第二成像结果;an imaging module, configured to provide a binocular camera to photograph a test object, to determine an imaging distance of the binocular camera, and to acquire a first imaging result and a second imaging result of the binocular camera corresponding to the imaging distance; 识别模块,用于识别所述第一成像结果和所述第二成像结果中的所述测试对象,获得所述测试对象在所述第一成像结果中的第一位置信息和在所述第二成像结果中的第二位置信息;an identification module, configured to identify the test object in the first imaging result and the second imaging result, and obtain first position information of the test object in the first imaging result and the test object in the second imaging result second position information in the imaging result; 分析模块,用于根据所述第一位置信息和所述第二位置信息,获得所述双目相机对应于所述成像距离的成像偏差分析结果。An analysis module, configured to obtain an imaging deviation analysis result of the binocular camera corresponding to the imaging distance according to the first position information and the second position information. 19.一种活体检测装置,其特征在于,包括:19. A living body detection device, comprising: 校准模块,用于根据双目相机的成像距离,利用根据权利要求18所述的成像偏差分析装置,获得所述双目相机对应于所述成像距离的成像偏差分析结果,并根据所述成像偏差分析结果校准所述双目相机;A calibration module, configured to obtain an imaging deviation analysis result of the binocular camera corresponding to the imaging distance by using the imaging deviation analysis device according to claim 18 according to the imaging distance of the binocular camera, and according to the imaging deviation analyzing the results to calibrate the binocular camera; 检测模块,用于利用校准后的所述双目相机针对满足所述成像距离的目标对象进行拍摄,获得所述目标对象的成像结果,并根据所述成像结果识别所述目标对象。A detection module, configured to use the calibrated binocular camera to shoot a target object that meets the imaging distance, obtain an imaging result of the target object, and identify the target object according to the imaging result.
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Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103903277A (en) * 2012-12-28 2014-07-02 重庆凯泽科技有限公司 Multi-ocular vision based data fusion algorithm
US20150009295A1 (en) * 2013-07-03 2015-01-08 Electronics And Telecommunications Research Institute Three-dimensional image acquisition apparatus and image processing method using the same
CN107169405A (en) * 2017-03-17 2017-09-15 上海云从企业发展有限公司 Method and device based on binocular camera vivo identification
CN108596122A (en) * 2018-04-28 2018-09-28 北京旷视科技有限公司 A kind of auth method, device, authentication machine and computer-readable medium
CN109212546A (en) * 2018-09-27 2019-01-15 北京伟景智能科技有限公司 The calculation method and device of binocular camera depth direction measurement error
CN109664317A (en) * 2019-01-24 2019-04-23 深圳勇艺达机器人有限公司 The grasping body system and method for robot
CN109767476A (en) * 2019-01-08 2019-05-17 像工场(深圳)科技有限公司 A kind of calibration of auto-focusing binocular camera and depth computing method
CN109889820A (en) * 2019-03-14 2019-06-14 深圳博时特科技有限公司 Detection method, device, storage medium and the terminal of binocular camera mould group
US20190273909A1 (en) * 2016-11-14 2019-09-05 SZ DJI Technology Co., Ltd. Methods and systems for selective sensor fusion
CN111046703A (en) * 2018-10-12 2020-04-21 杭州海康威视数字技术股份有限公司 Face anti-counterfeiting detection method and device and multi-view camera
CN111209870A (en) * 2020-01-09 2020-05-29 杭州涂鸦信息技术有限公司 Binocular living body camera rapid registration method, system and device thereof
CN111414831A (en) * 2020-03-13 2020-07-14 深圳市商汤科技有限公司 Monitoring method and system, electronic device and storage medium
CN111444744A (en) * 2018-12-29 2020-07-24 北京市商汤科技开发有限公司 Living body detection method, living body detection device, and storage medium
CN112257538A (en) * 2020-10-15 2021-01-22 杭州锐颖科技有限公司 Living body detection method and device based on binocular depth information and storage medium
CN112257641A (en) * 2020-10-30 2021-01-22 中电万维信息技术有限责任公司 Face recognition living body detection method

Patent Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103903277A (en) * 2012-12-28 2014-07-02 重庆凯泽科技有限公司 Multi-ocular vision based data fusion algorithm
US20150009295A1 (en) * 2013-07-03 2015-01-08 Electronics And Telecommunications Research Institute Three-dimensional image acquisition apparatus and image processing method using the same
US20190273909A1 (en) * 2016-11-14 2019-09-05 SZ DJI Technology Co., Ltd. Methods and systems for selective sensor fusion
CN107169405A (en) * 2017-03-17 2017-09-15 上海云从企业发展有限公司 Method and device based on binocular camera vivo identification
CN108596122A (en) * 2018-04-28 2018-09-28 北京旷视科技有限公司 A kind of auth method, device, authentication machine and computer-readable medium
CN109212546A (en) * 2018-09-27 2019-01-15 北京伟景智能科技有限公司 The calculation method and device of binocular camera depth direction measurement error
CN111046703A (en) * 2018-10-12 2020-04-21 杭州海康威视数字技术股份有限公司 Face anti-counterfeiting detection method and device and multi-view camera
CN111444744A (en) * 2018-12-29 2020-07-24 北京市商汤科技开发有限公司 Living body detection method, living body detection device, and storage medium
CN109767476A (en) * 2019-01-08 2019-05-17 像工场(深圳)科技有限公司 A kind of calibration of auto-focusing binocular camera and depth computing method
CN109664317A (en) * 2019-01-24 2019-04-23 深圳勇艺达机器人有限公司 The grasping body system and method for robot
CN109889820A (en) * 2019-03-14 2019-06-14 深圳博时特科技有限公司 Detection method, device, storage medium and the terminal of binocular camera mould group
CN111209870A (en) * 2020-01-09 2020-05-29 杭州涂鸦信息技术有限公司 Binocular living body camera rapid registration method, system and device thereof
CN111414831A (en) * 2020-03-13 2020-07-14 深圳市商汤科技有限公司 Monitoring method and system, electronic device and storage medium
CN112257538A (en) * 2020-10-15 2021-01-22 杭州锐颖科技有限公司 Living body detection method and device based on binocular depth information and storage medium
CN112257641A (en) * 2020-10-30 2021-01-22 中电万维信息技术有限责任公司 Face recognition living body detection method

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