CN117058738B - Remote face detection and recognition method and system for mobile law enforcement equipment - Google Patents
Remote face detection and recognition method and system for mobile law enforcement equipment Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000001514 detection method Methods 0.000 title claims abstract description 24
- 238000003709 image segmentation Methods 0.000 claims abstract description 28
- 238000013507 mapping Methods 0.000 claims description 24
- 238000005457 optimization Methods 0.000 claims description 24
- 230000007613 environmental effect Effects 0.000 claims description 20
- 238000013441 quality evaluation Methods 0.000 claims description 18
- 230000033001 locomotion Effects 0.000 claims description 14
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
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- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
Abstract
The embodiment of the invention discloses a method and a system for detecting and identifying a long-distance face of mobile law enforcement equipment, wherein the method comprises the following steps: detecting a target distance between the target object and the mobile law enforcement device by a distance sensor; when the target distance is larger than the preset distance, determining a target shooting parameter corresponding to the target distance; image segmentation is carried out on the first image to obtain a target face area and a target background area; determining target relation parameters between a target face region and a target background region; determining target face recognition parameters according to the target relation parameters and the target distance; and identifying the target face area according to the target face identification parameters to obtain a target face identification result. The embodiment of the invention can improve the detection and recognition effects of the mobile law enforcement equipment on the long-distance human face.
Description
Technical Field
The invention relates to the technical field of electronics, in particular to a method and a system for detecting and identifying a long-distance face of mobile law enforcement equipment.
Background
With the advancement of society, the popularity of electronic law enforcement will become a necessary trend. From the perspective of improving law enforcement and saving law enforcement resources, electronic law enforcement meets social demands, more and more industries are adopting electronic government manners, and mobile law enforcement equipment is more and more popular, but currently, the mobile law enforcement equipment has poor detection and recognition effects on long-distance faces, so that the problem of how to improve the detection and recognition effects on long-distance faces of the mobile law enforcement equipment is urgently solved.
Disclosure of Invention
The embodiment of the invention provides a method and a system for detecting and identifying a long-distance face of mobile law enforcement equipment, which are beneficial to improving the detection and identification effect of the mobile law enforcement equipment on the long-distance face.
In a first aspect, an embodiment of the present invention provides a method for detecting and identifying a long-distance face moving by a mobile law enforcement device, which is applied to the mobile law enforcement device, where the mobile law enforcement device includes a camera and a distance sensor; the method comprises the following steps:
detecting a target distance between a target object and the mobile law enforcement device by the distance sensor;
when the target distance is greater than a preset distance, determining a target shooting parameter corresponding to the target distance;
shooting the target object according to the target shooting parameters to obtain a first image;
Image segmentation is carried out on the first image to obtain a target face area and a target background area;
Determining a target relation parameter between the target face region and the target background region;
Determining target face recognition parameters according to the target relation parameters and the target distance;
and identifying the target face area according to the target face identification parameters to obtain a target face identification result.
In a second aspect, an embodiment of the present invention provides a remote face detection and recognition system for mobile law enforcement equipment, which is applied to mobile law enforcement equipment, where the mobile law enforcement equipment includes a camera and a distance sensor; the system comprises: a detection unit, a determination unit, a shooting unit, a segmentation unit and an identification unit, wherein,
The detection unit is used for detecting a target distance between a target object and the mobile law enforcement equipment through the distance sensor;
the determining unit is used for determining a target shooting parameter corresponding to the target distance when the target distance is larger than a preset distance;
The shooting unit is used for shooting the target object according to the target shooting parameters to obtain a first image;
The segmentation unit is used for carrying out image segmentation on the first image to obtain a target face area and a target background area;
the determining unit is further used for determining a target relation parameter between the target face area and the target background area; determining target face recognition parameters according to the target relation parameters and the target distance;
and the identification unit is used for identifying the target face area according to the target face identification parameters to obtain a target face identification result.
The embodiment of the invention has the following beneficial effects:
it can be seen that the remote face detection and recognition method and system for mobile law enforcement equipment movement described in the embodiments of the present invention are applied to mobile law enforcement equipment, the mobile law enforcement equipment includes a camera and a distance sensor, the distance sensor detects the target distance between the target object and the mobile law enforcement equipment, when the target distance is greater than the preset distance, the target shooting parameter corresponding to the target distance is determined, the target object is shot according to the target shooting parameter to obtain a first image, the first image is subjected to image segmentation to obtain a target face area and a target background area, the target relation parameter between the target face area and the target background area is determined, the target face recognition parameter is determined according to the target relation parameter and the target distance, and the target face area is recognized according to the target face recognition parameter to obtain a target face recognition result.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for detecting and identifying a long-distance face of a mobile law enforcement device according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a mobile law enforcement device according to an embodiment of the present invention;
Fig. 3 is a functional unit block diagram of a remote face detection and recognition system for mobile law enforcement equipment according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Embodiments of the present invention are described in detail below.
Referring to fig. 1, fig. 1 is a flow chart of a method for detecting and identifying a long-distance face of a mobile law enforcement device according to an embodiment of the present invention, which is applied to the mobile law enforcement device, wherein the mobile law enforcement device includes a camera and a distance sensor; the method can comprise the following steps:
101. and detecting the target distance between the target object and the mobile law enforcement equipment through the distance sensor.
In the embodiment of the invention, the mobile law enforcement device may include a camera and a distance sensor, the number of cameras may be one or more, and the camera may include at least one of the following: infrared cameras, visible cameras, and the like, are not limited herein.
In particular implementations, the mobile law enforcement device may include any device for law enforcement, which may include law enforcement instruments, mobile law enforcement boxes, mobile law enforcement terminals, and the like, without limitation.
In a specific implementation, the target object may be a person or an animal, or any other suspicious object, and during the moving process of the mobile law enforcement device, the mobile law enforcement device may detect the target distance between the target object and the mobile law enforcement device through the distance sensor.
102. And when the target distance is greater than a preset distance, determining a target shooting parameter corresponding to the target distance.
In the embodiment of the invention, the preset distance can be preset or the system defaults.
In the specific implementation, when the target distance is greater than the preset distance, the mobile law enforcement device is long-distance shooting, and then the target shooting parameters corresponding to the target distance can be determined according to the mapping relation between the preset distance and the shooting parameters stored in advance, so that the shooting effect is improved.
Optionally, the determining, in step 102, the target shooting parameter corresponding to the target distance may include the following steps:
21. determining a target relative motion rate between the mobile execution device and the target object;
22. Determining a reference shooting parameter corresponding to the target distance according to a mapping relation between a preset distance and the shooting parameter;
23. obtaining a target environmental parameter, the target environmental parameter comprising at least one of: temperature, weather, atmospheric pressure, ambient light;
24. Determining a first adjustment parameter corresponding to the target environmental parameter;
25. Determining a first trimming parameter corresponding to the target;
26. And adjusting the reference shooting parameters according to the first adjusting parameters and the first fine tuning parameters to obtain the target shooting parameters.
In the embodiment of the invention, the target relative motion rate between the mobile execution device and the target object can be determined, and the mapping relation between the preset distance and the shooting parameters can be preset, wherein the shooting parameters can comprise at least one of the following: exposure time, photographing mode, white balance parameters, and the like, are not limited herein. Further, a reference photographing parameter corresponding to the target distance may be determined based on the mapping relation.
Further, a target environmental parameter may be obtained, the target environmental parameter including at least one of: the temperature, weather, atmospheric pressure, ambient light brightness and the like are not limited herein, a mapping relation between preset environmental parameters and adjustment parameters can be preset, further, a first adjustment parameter corresponding to a target environmental parameter can be determined based on the mapping relation, a mapping relation between a preset relative movement rate and a fine adjustment parameter can be preset, a first fine adjustment parameter corresponding to the target relative movement rate is determined based on the mapping relation, and then the reference shooting parameter is adjusted according to the first adjustment parameter and the first fine adjustment parameter, so that the target shooting parameter is obtained, and the method specifically comprises the following steps:
target shooting parameter= (1+first adjustment parameter + (1+first trimming parameter)) = reference shooting parameter
Wherein, the value range of the first adjusting parameter is-0.1 to 0.1, and the value range of the first fine adjusting parameter is-0.02 to 0.02.
In this example, can confirm the shooting parameter corresponding with the distance, can also confirm corresponding adjustment parameter based on environmental parameter to and finely tune adjustment parameter based on relative motion rate, make adjustment parameter to shooting parameter's regulation more accord with current scene, help promoting image shooting effect, and then help promoting follow-up face detection and discernment precision.
103. And shooting the target object according to the target shooting parameters to obtain a first image.
According to the embodiment of the invention, the mobile law enforcement equipment can shoot the target object according to the target shooting parameters to obtain the first image, and can ensure that a high-quality image effect is obtained under long-distance shooting.
104. And carrying out image segmentation on the first image to obtain a target face region and a target background region.
In the embodiment of the invention, the mobile law enforcement equipment can adopt an image segmentation algorithm to carry out image segmentation on the first image so as to obtain the target face area and the target background area. The target background area may refer to a non-face area of the human body or other areas of the first image other than the target face area.
Optionally, in step 104, image segmentation is performed on the first image to obtain a target face area and a target background area, which may include the following steps:
41. Determining a target relative angle between the target object and the camera;
42. determining a target image segmentation algorithm corresponding to the target environment parameter;
43. acquiring original image segmentation algorithm control parameters corresponding to the target image segmentation algorithm;
44. Determining a target influence factor of a relative angle to the target;
45. Processing the original image segmentation algorithm control parameters according to the target influence factors to obtain target image segmentation algorithm control parameters;
46. And dividing the first image according to the target image dividing algorithm and the target image dividing algorithm control parameter to obtain the target face region and the target background region.
In the embodiment of the invention, the mobile law enforcement equipment can determine the target relative angle between the target object and the camera, can pre-store the mapping relation between the preset environment parameter and the image segmentation algorithm, determine the target image segmentation algorithm corresponding to the target environment parameter based on the mapping relation, adapt different image segmentation algorithms to different environment parameters, and help to ensure that the optimal image segmentation algorithm is selected under each environment.
Further, original image segmentation algorithm control parameters corresponding to the target image segmentation algorithm can be obtained, and different relative angles indicate that the region of the human face and the face shape of the human face are different, so that the target influence factor corresponding to the target relative angle can be determined according to the mapping relation between the preset relative angle and the influence factor, the original image segmentation algorithm control parameters are processed according to the target influence factor to obtain the target image segmentation algorithm control parameters, the image segmentation algorithm control parameters corresponding to the region of the human face and the face shape of the human face can be obtained, the first image is segmented according to the target image segmentation algorithm and the target image segmentation algorithm control parameters to obtain the target face region and the target background region, the human face can be accurately distinguished from the non-human face, and the segmentation accuracy of the human face image can be ensured.
105. And determining target relation parameters between the target face area and the target background area.
In the embodiment of the invention, the area ratio between the target face area and the target background area can be determined, and the area ratio can be used as a target relation parameter, or the image quality ratio between the target face area and the target background area can be used as a target relation parameter.
106. And determining target face recognition parameters according to the target relation parameters and the target distance.
In the embodiment of the invention, the target face recognition parameters can comprise a target face recognition algorithm and a target face recognition algorithm control parameter, wherein the target face recognition algorithm control parameter is used for controlling the recognition rate, recognition accuracy, recognition range and the like of the target face recognition algorithm.
In the specific implementation, the target face recognition parameters can be determined according to the target relation parameters and the target distance, so that the target face recognition parameters corresponding to the relation between the target face region and the target background region and the distance can be obtained, and the face recognition accuracy is guaranteed.
Optionally, the step 106 of determining the target face recognition parameter according to the target relation parameter and the target distance may include the following steps:
61. Determining a target face recognition algorithm corresponding to the target distance;
62. Acquiring default face recognition algorithm control parameters corresponding to the target face recognition algorithm;
63. Determining a target optimization factor corresponding to the target relation parameter;
64. and optimizing the default face recognition algorithm control parameters according to the target optimization factors to obtain the target face recognition algorithm control parameters.
In the embodiment of the invention, the mapping relation between the preset distance and the face recognition algorithm can be stored in advance, and further, the target face recognition algorithm corresponding to the target distance can be determined based on the mapping relation, and the default face recognition algorithm control parameter corresponding to the target face recognition algorithm can be acquired, wherein the default face recognition algorithm control parameter is used for controlling the recognition rate, recognition accuracy and the like of the face recognition algorithm.
Then, according to a mapping relation between preset relation parameters and optimization factors, a target optimization factor corresponding to the target relation parameters can be determined based on the mapping relation, and then, a default face recognition algorithm control parameter is optimized according to the target optimization factor to obtain the target face recognition algorithm control parameter, specifically, the target face recognition algorithm control parameter= (1+the default face recognition algorithm control parameter), the value range of the default face recognition algorithm control parameter can be-0.1, and further, the face recognition algorithm control parameter can be optimized based on the relative relation between the face region and the background region, so that face recognition accuracy is guaranteed.
Optionally, in the step 64, the optimizing process is performed on the default face recognition algorithm control parameter according to the target optimizing factor to obtain the target face recognition algorithm control parameter, which may include the following steps:
641. optimizing the default face recognition algorithm control parameters according to the target optimization factors to obtain first face recognition algorithm control parameters;
642. Dividing the target face area into a plurality of areas, wherein the area of each area is equal;
643. determining an image quality evaluation value of each of the plurality of areas to obtain a plurality of image quality evaluation values;
644. determining target mean square deviations of the plurality of image quality evaluation values;
645. And adjusting the first face recognition algorithm control parameter according to the target mean square error to obtain the target face recognition algorithm control parameter.
In the embodiment of the invention, the default face recognition algorithm control parameter is optimized according to the target optimization factor to obtain the first face recognition algorithm control parameter, specifically, the first face recognition algorithm control parameter= (1+default face recognition algorithm control parameter), and the value range of the default face recognition algorithm control parameter can be-0.1.
Then, the target face area can be divided into a plurality of areas, the areas of the areas are equal, further, the image quality evaluation value of each area in the plurality of areas can be determined, a plurality of image quality evaluation values are obtained, then the target mean square error of the plurality of image quality evaluation values is determined, the mean square error reflects the consistency of the image quality of the face area, the consistency of the image quality of the face area reflects the face recognition accuracy to a certain extent, the mapping relation between the preset mean square error and the adjustment parameters can be stored in advance, the target adjustment parameters corresponding to the target mean square error are determined based on the mapping relation, the first face recognition algorithm control parameters are adjusted through the target adjustment parameters, the target face recognition algorithm control parameters are obtained, further, the face recognition algorithm control parameters can be adjusted based on the consistency of the image quality of the face area, the face recognition effect is more consistent with the actual condition of the face, and the face recognition accuracy is guaranteed.
Optionally, when the first face recognition algorithm control parameter includes k parameters, k is an integer greater than 1, and step 645 adjusts the first face recognition algorithm control parameter according to the target mean square error to obtain the target face recognition algorithm control parameter, may include the following steps:
S1, determining adjustable parameters and non-adjustable parameters in the k parameters to obtain a adjustable parameters and b non-adjustable parameters, wherein k=a+b, and a and b are positive integers;
S2, determining a target adjustment parameter corresponding to the target mean square error;
S3, adjusting the a adjustable parameters according to the target adjustment parameters to obtain a target adjustable parameters;
s4, determining the target face recognition algorithm control parameters according to the a target adjustable parameters and the b non-adjustable parameters.
In the embodiment of the invention, when the first face recognition algorithm control parameter comprises k parameters, k is an integer greater than 1, then the adjustable parameters and the non-adjustable parameters in the k parameters can be determined to obtain a adjustable parameters and b non-adjustable parameters, k=a+b, a and b are positive integers, in specific implementation, each parameter in the k parameters can correspond to an adjustable priority, the distribution density of target feature points of the target face area can be determined, then the target priority threshold corresponding to the distribution density of the target feature points is determined according to the mapping relation between the preset distribution density of the feature points and the priority threshold, and the k parameters are divided into a adjustable parameter and b non-adjustable parameters based on the target priority threshold to obtain a adjustable parameter and b non-adjustable parameters.
Further, a mapping relation between preset mean square error and adjustment parameters can be stored in advance, further, target adjustment parameters corresponding to the target mean square error can be determined based on the mapping relation, then a target adjustable parameters are adjusted according to the target adjustment parameters to obtain a target adjustable parameters, and then target face recognition algorithm control parameters are determined according to the a target adjustable parameters and b non-adjustable parameters, so that the adjustable parameters of the face recognition algorithm control parameters can be adjusted, the face recognition effect is more remarkable, the non-adjustable parameters are not adjusted, the face recognition effect is guaranteed to be the opposite, and accordingly face recognition accuracy is guaranteed.
107. And identifying the target face area according to the target face identification parameters to obtain a target face identification result.
In the embodiment of the invention, the target face area can be identified according to the target face identification parameters to obtain the target face identification result, and the target face identification parameters corresponding to the relationship between the target face area and the target background area and the distance are obtained, so that the face identification accuracy is guaranteed.
It can be seen that the remote face detection and recognition method for mobile law enforcement equipment described in the embodiment of the present invention is applied to mobile law enforcement equipment, the mobile law enforcement equipment includes a camera and a distance sensor, the distance sensor detects a target distance between a target object and the mobile law enforcement equipment, when the target distance is greater than a preset distance, a target shooting parameter corresponding to the target distance is determined, the target object is shot according to the target shooting parameter to obtain a first image, the first image is segmented to obtain a target face area and a target background area, a target relation parameter between the target face area and the target background area is determined, the target face area is recognized according to the target relation parameter and the target distance, and a target face recognition result is obtained.
In accordance with the above embodiment, referring to fig. 2, fig. 2 is a schematic structural diagram of a mobile law enforcement device according to an embodiment of the present invention, where the mobile law enforcement device includes a processor, a memory, a communication interface, and one or more programs, and the one or more programs are stored in the memory and configured to be executed by the processor, and the mobile law enforcement device further includes a camera and a distance sensor, where in the embodiment of the present invention, the programs include instructions for performing the following steps:
detecting a target distance between a target object and the mobile law enforcement device by the distance sensor;
when the target distance is greater than a preset distance, determining a target shooting parameter corresponding to the target distance;
shooting the target object according to the target shooting parameters to obtain a first image;
Image segmentation is carried out on the first image to obtain a target face area and a target background area;
Determining a target relation parameter between the target face region and the target background region;
Determining target face recognition parameters according to the target relation parameters and the target distance;
and identifying the target face area according to the target face identification parameters to obtain a target face identification result.
Optionally, in the determining the target shooting parameter corresponding to the target distance, the program includes instructions for:
Determining a target relative motion rate between the mobile execution device and the target object;
determining a reference shooting parameter corresponding to the target distance according to a mapping relation between a preset distance and the shooting parameter;
obtaining a target environmental parameter, the target environmental parameter comprising at least one of: temperature, weather, atmospheric pressure, ambient light;
determining a first adjustment parameter corresponding to the target environmental parameter;
Determining a first fine tuning parameter corresponding to the target relative motion rate;
And adjusting the reference shooting parameters according to the first adjusting parameters and the first fine tuning parameters to obtain the target shooting parameters.
Optionally, in the determining the target face recognition parameter according to the target relation parameter and the target distance, the program includes instructions for:
determining a target face recognition algorithm corresponding to the target distance;
Acquiring default face recognition algorithm control parameters corresponding to the target face recognition algorithm;
determining a target optimization factor corresponding to the target relation parameter;
And optimizing the default face recognition algorithm control parameters according to the target optimization factors to obtain the target face recognition algorithm control parameters.
Optionally, in the aspect that the optimization processing is performed on the default face recognition algorithm control parameter according to the target optimization factor to obtain the target face recognition algorithm control parameter, the program includes instructions for executing the following steps:
Optimizing the default face recognition algorithm control parameters according to the target optimization factors to obtain first face recognition algorithm control parameters;
dividing the target face area into a plurality of areas, wherein the area of each area is equal;
Determining an image quality evaluation value of each of the plurality of areas to obtain a plurality of image quality evaluation values;
Determining target mean square deviations of the plurality of image quality evaluation values;
And adjusting the first face recognition algorithm control parameter according to the target mean square error to obtain the target face recognition algorithm control parameter.
Optionally, when the first face recognition algorithm control parameter includes k parameters, k is an integer greater than 1, and in the aspect of adjusting the first face recognition algorithm control parameter according to the target mean square error to obtain the target face recognition algorithm control parameter, the program includes instructions for executing the following steps:
Determining adjustable parameters and non-adjustable parameters in the k parameters to obtain a adjustable parameters and b non-adjustable parameters, wherein k=a+b, and a and b are positive integers;
determining a target adjustment parameter corresponding to the target mean square error;
adjusting the a adjustable parameters according to the target adjustment parameters to obtain a target adjustable parameters;
And determining the target face recognition algorithm control parameters according to the a target adjustable parameters and the b non-adjustable parameters.
It can be seen that, in the mobile law enforcement device described in the embodiment of the present invention, the mobile law enforcement device includes a camera and a distance sensor, the distance sensor detects the target distance between the target object and the mobile law enforcement device, when the target distance is greater than the preset distance, the target shooting parameter corresponding to the target distance is determined, the target object is shot according to the target shooting parameter to obtain a first image, the first image is segmented to obtain a target face area and a target background area, the target relation parameter between the target face area and the target background area is determined, the target face recognition parameter is determined according to the target relation parameter and the target distance, and the target face recognition parameter is identified according to the target face recognition parameter to obtain a target face recognition result.
FIG. 3 is a functional block diagram of a mobile law enforcement device mobile remote face detection and recognition system 300 according to an embodiment of the present invention, where the mobile law enforcement device mobile remote face detection and recognition system 300 is applied to a mobile law enforcement device, and the mobile law enforcement device includes a camera and a distance sensor; the remote face detection and recognition system 300 for mobile law enforcement device comprises: a detection unit 301, a determination unit 302, a photographing unit 303, a segmentation unit 304, and an identification unit 305, wherein,
The detection unit 301 is configured to detect, by using the distance sensor, a target distance between a target object and the mobile law enforcement device;
the determining unit 302 is configured to determine a target shooting parameter corresponding to the target distance when the target distance is greater than a preset distance;
the shooting unit 303 is configured to shoot the target object according to the target shooting parameter, so as to obtain a first image;
The segmentation unit 304 is configured to perform image segmentation on the first image to obtain a target face area and a target background area;
The determining unit 302 is further configured to determine a target relationship parameter between the target face area and the target background area; determining target face recognition parameters according to the target relation parameters and the target distance;
the identifying unit 305 is configured to identify the target face area according to the target face identification parameter, so as to obtain a target face identification result.
Optionally, in the aspect of determining the target shooting parameter corresponding to the target distance, the determining unit 302 is specifically configured to:
Determining a target relative motion rate between the mobile execution device and the target object;
determining a reference shooting parameter corresponding to the target distance according to a mapping relation between a preset distance and the shooting parameter;
obtaining a target environmental parameter, the target environmental parameter comprising at least one of: temperature, weather, atmospheric pressure, ambient light;
determining a first adjustment parameter corresponding to the target environmental parameter;
Determining a first fine tuning parameter corresponding to the target relative motion rate;
And adjusting the reference shooting parameters according to the first adjusting parameters and the first fine tuning parameters to obtain the target shooting parameters.
Optionally, in the aspect of determining the target face recognition parameter according to the target relation parameter and the target distance, the determining unit 302 is specifically configured to:
determining a target face recognition algorithm corresponding to the target distance;
Acquiring default face recognition algorithm control parameters corresponding to the target face recognition algorithm;
determining a target optimization factor corresponding to the target relation parameter;
And optimizing the default face recognition algorithm control parameters according to the target optimization factors to obtain the target face recognition algorithm control parameters.
Optionally, in the aspect that the optimization processing is performed on the default face recognition algorithm control parameter according to the target optimization factor to obtain the target face recognition algorithm control parameter, the determining unit 302 is specifically configured to:
Optimizing the default face recognition algorithm control parameters according to the target optimization factors to obtain first face recognition algorithm control parameters;
dividing the target face area into a plurality of areas, wherein the area of each area is equal;
Determining an image quality evaluation value of each of the plurality of areas to obtain a plurality of image quality evaluation values;
Determining target mean square deviations of the plurality of image quality evaluation values;
And adjusting the first face recognition algorithm control parameter according to the target mean square error to obtain the target face recognition algorithm control parameter.
Optionally, when the first face recognition algorithm control parameters include k parameters, k is an integer greater than 1, and in the aspect of adjusting the first face recognition algorithm control parameters according to the target mean square error to obtain the target face recognition algorithm control parameters, the determining unit 302 is specifically configured to:
Determining adjustable parameters and non-adjustable parameters in the k parameters to obtain a adjustable parameters and b non-adjustable parameters, wherein k=a+b, and a and b are positive integers;
determining a target adjustment parameter corresponding to the target mean square error;
adjusting the a adjustable parameters according to the target adjustment parameters to obtain a target adjustable parameters;
And determining the target face recognition algorithm control parameters according to the a target adjustable parameters and the b non-adjustable parameters.
It can be seen that the remote face detection and recognition system for mobile law enforcement equipment described in the embodiment of the present invention is applied to mobile law enforcement equipment, the mobile law enforcement equipment includes a camera and a distance sensor, the distance sensor detects a target distance between a target object and the mobile law enforcement equipment, when the target distance is greater than a preset distance, a target shooting parameter corresponding to the target distance is determined, the target object is shot according to the target shooting parameter to obtain a first image, the first image is segmented to obtain a target face area and a target background area, a target relation parameter between the target face area and the target background area is determined, the target face area is recognized according to the target relation parameter and the target distance, and a target face recognition result is obtained.
It may be appreciated that the functions of each program module of the mobile remote face detection and recognition system of the mobile law enforcement device of the present embodiment may be implemented according to the method in the foregoing method embodiment, and the specific implementation process may refer to the relevant description of the foregoing method embodiment and will not be repeated herein.
The embodiment of the present invention also provides a computer storage medium storing a computer program for electronic data exchange, where the computer program causes a computer to execute some or all of the steps of any one of the methods described in the above method embodiments.
Embodiments of the present invention also provide a computer program product comprising a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform part or all of the steps of any one of the methods described in the method embodiments above. The computer program product may be a software installation package.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present invention is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present invention. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, such as the above-described division of units, merely a division of logic functions, and there may be additional manners of dividing in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present invention may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the above-mentioned method of the various embodiments of the present invention. And the aforementioned memory includes: a usb disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
The foregoing has outlined rather broadly the more detailed description of embodiments of the invention, wherein the principles and embodiments of the invention are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (8)
1. The face detection and recognition method for the mobile law enforcement equipment is characterized by being applied to the mobile law enforcement equipment, wherein the mobile law enforcement equipment comprises a camera and a distance sensor; the method comprises the following steps:
detecting a target distance between a target object and the mobile law enforcement device by the distance sensor;
when the target distance is greater than a preset distance, determining a target shooting parameter corresponding to the target distance;
shooting the target object according to the target shooting parameters to obtain a first image;
Image segmentation is carried out on the first image to obtain a target face area and a target background area;
Determining a target relation parameter between the target face region and the target background region;
Determining target face recognition parameters according to the target relation parameters and the target distance;
Identifying the target face area according to the target face identification parameters to obtain a target face identification result;
The determining the target face recognition parameter according to the target relation parameter and the target distance includes:
determining a target face recognition algorithm corresponding to the target distance;
Acquiring default face recognition algorithm control parameters corresponding to the target face recognition algorithm;
determining a target optimization factor corresponding to the target relation parameter;
And optimizing the default face recognition algorithm control parameters according to the target optimization factors to obtain the target face recognition algorithm control parameters.
2. The method of claim 1, wherein the determining the target capture parameter corresponding to the target distance comprises:
determining a target relative rate of motion between the mobile enforcement device and the target object;
determining a reference shooting parameter corresponding to the target distance according to a mapping relation between a preset distance and the shooting parameter;
obtaining a target environmental parameter, the target environmental parameter comprising at least one of: temperature, weather, atmospheric pressure, ambient light;
determining a first adjustment parameter corresponding to the target environmental parameter;
Determining a first fine tuning parameter corresponding to the target relative motion rate;
And adjusting the reference shooting parameters according to the first adjusting parameters and the first fine tuning parameters to obtain the target shooting parameters.
3. The method according to claim 1, wherein the optimizing the default face recognition algorithm control parameter according to the target optimization factor to obtain the target face recognition algorithm control parameter includes:
Optimizing the default face recognition algorithm control parameters according to the target optimization factors to obtain first face recognition algorithm control parameters;
dividing the target face area into a plurality of areas, wherein the area of each area is equal;
Determining an image quality evaluation value of each of the plurality of areas to obtain a plurality of image quality evaluation values;
Determining target mean square deviations of the plurality of image quality evaluation values;
And adjusting the first face recognition algorithm control parameter according to the target mean square error to obtain the target face recognition algorithm control parameter.
4. A method according to claim 3, wherein when the first face recognition algorithm control parameters include k parameters, k is an integer greater than 1, and the adjusting the first face recognition algorithm control parameters according to the target mean square error to obtain the target face recognition algorithm control parameters includes:
Determining adjustable parameters and non-adjustable parameters in the k parameters to obtain a adjustable parameters and b non-adjustable parameters, wherein k=a+b, and a and b are positive integers;
determining a target adjustment parameter corresponding to the target mean square error;
adjusting the a adjustable parameters according to the target adjustment parameters to obtain a target adjustable parameters;
And determining the target face recognition algorithm control parameters according to the a target adjustable parameters and the b non-adjustable parameters.
5. The face detection and recognition system for the mobile law enforcement equipment is characterized by being applied to the mobile law enforcement equipment, wherein the mobile law enforcement equipment comprises a camera and a distance sensor; the system comprises: a detection unit, a determination unit, a shooting unit, a segmentation unit and an identification unit, wherein,
The detection unit is used for detecting a target distance between a target object and the mobile law enforcement equipment through the distance sensor;
the determining unit is used for determining a target shooting parameter corresponding to the target distance when the target distance is larger than a preset distance;
The shooting unit is used for shooting the target object according to the target shooting parameters to obtain a first image;
The segmentation unit is used for carrying out image segmentation on the first image to obtain a target face area and a target background area;
the determining unit is further used for determining a target relation parameter between the target face area and the target background area; determining target face recognition parameters according to the target relation parameters and the target distance;
the identification unit is used for identifying the target face area according to the target face identification parameters to obtain a target face identification result;
the determining unit is specifically configured to, in the aspect of determining the target face recognition parameter according to the target relation parameter and the target distance:
determining a target face recognition algorithm corresponding to the target distance;
Acquiring default face recognition algorithm control parameters corresponding to the target face recognition algorithm;
determining a target optimization factor corresponding to the target relation parameter;
And optimizing the default face recognition algorithm control parameters according to the target optimization factors to obtain the target face recognition algorithm control parameters.
6. The system according to claim 5, wherein in the determining the target shooting parameter corresponding to the target distance, the determining unit is specifically configured to:
determining a target relative rate of motion between the mobile enforcement device and the target object;
determining a reference shooting parameter corresponding to the target distance according to a mapping relation between a preset distance and the shooting parameter;
obtaining a target environmental parameter, the target environmental parameter comprising at least one of: temperature, weather, atmospheric pressure, ambient light;
determining a first adjustment parameter corresponding to the target environmental parameter;
Determining a first fine tuning parameter corresponding to the target relative motion rate;
And adjusting the reference shooting parameters according to the first adjusting parameters and the first fine tuning parameters to obtain the target shooting parameters.
7. The system according to claim 5, wherein the determining unit is specifically configured to:
Optimizing the default face recognition algorithm control parameters according to the target optimization factors to obtain first face recognition algorithm control parameters;
dividing the target face area into a plurality of areas, wherein the area of each area is equal;
Determining an image quality evaluation value of each of the plurality of areas to obtain a plurality of image quality evaluation values;
Determining target mean square deviations of the plurality of image quality evaluation values;
And adjusting the first face recognition algorithm control parameter according to the target mean square error to obtain the target face recognition algorithm control parameter.
8. The system according to claim 7, wherein when the first face recognition algorithm control parameters include k parameters, k is an integer greater than 1, and the determining unit is specifically configured to:
Determining adjustable parameters and non-adjustable parameters in the k parameters to obtain a adjustable parameters and b non-adjustable parameters, wherein k=a+b, and a and b are positive integers;
determining a target adjustment parameter corresponding to the target mean square error;
adjusting the a adjustable parameters according to the target adjustment parameters to obtain a target adjustable parameters;
And determining the target face recognition algorithm control parameters according to the a target adjustable parameters and the b non-adjustable parameters.
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