CN111832354A - Target object age identification method and device and electronic equipment - Google Patents
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Abstract
The embodiment of the disclosure provides a target object age identification method, a target object age identification device and electronic equipment, which belong to the technical field of data processing, and the method comprises the following steps: obtaining a plurality of images in a video file containing a target object, the target object having an edge perimeter on each of the plurality of images; determining a type value of an image containing a target object based on the edge perimeter; predicting the classification of each image in the plurality of images through a prediction model to obtain a classification prediction value of each image; training the prediction model based on the type value and the classification predicted value of each image to enable the prediction precision of the prediction model to reach a preset value; and identifying the age of the target object in the newly acquired video file based on the trained prediction model. Through the scheme disclosed by the invention, the accuracy of identifying the age of the image target object is improved.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for identifying an age of a target object, and an electronic device.
Background
With the development of face recognition technology, the demand for face attribute recognition is higher and higher, especially for age recognition of faces. In the process of age identification, a training sample is usually associated and matched with an age label, that is, one sample corresponds to one age label. Thus, by training enough samples, the age of the newly input face can be predicted.
Age recognition based on human faces relates to various technologies, including support vector machines, ensemble learning, deep neural networks and the like, which have high requirements on training samples, and the quality of the training samples is unbalanced due to different acquisition equipment and application environments.
As an application requirement, it is desirable to be able to judge the age of one or more users present in a video from a piece of video captured by the user. Due to the problem of shooting angle or picture quality, the predicted user age fluctuates back and forth in a section, and the accuracy and stability are not sufficient.
Disclosure of Invention
In view of the above, embodiments of the present disclosure provide a method, an apparatus, and an electronic device for identifying an age of a target object, which at least partially solve the problems in the prior art.
In a first aspect, an embodiment of the present disclosure provides a target object age identification method, including:
obtaining a plurality of images in a video file containing a target object, the target object having an edge perimeter on each of the plurality of images;
determining a type value of an image containing a target object based on the edge perimeter;
predicting the classification of each image in the plurality of images through a prediction model to obtain a classification prediction value of each image;
training the prediction model based on the type value and the classification predicted value of the image to enable the prediction precision of the prediction model to reach a preset value;
and identifying the age of the target object in the newly acquired video file based on the trained prediction model.
According to a specific implementation manner of the embodiment of the present disclosure, the acquiring a plurality of images including a target object in a video file includes:
analyzing the video file to obtain a plurality of video frames;
and selecting an image containing the target object from the plurality of video frames to form the plurality of images.
According to a specific implementation manner of the embodiment of the present disclosure, the determining a type value of an image including a target object based on the edge perimeter includes:
setting the image with the edge perimeter larger than a preset threshold value as a first type value;
and setting the image with the edge perimeter smaller than a preset threshold value as a second type value.
According to a specific implementation manner of the embodiment of the present disclosure, the predicting the classification of each of the plurality of images by using the prediction model to obtain a classification prediction value of each image includes:
setting a neural network model g corresponding to the prediction model, wherein the neural network model g comprises a convolutional layer, a pooling layer and a sampling layer;
and generating a classification prediction value of the image by using the neural network model g.
According to a specific implementation manner of the embodiment of the present disclosure, the generating the classification prediction value of the image by using the neural network model g includes:
and setting the number of the convolutional layers and the sampling layers in the neural network model g to be respectively more than 2, and performing pooling processing on the image by adopting a maximum pooling mode after the convolutional layers.
According to a specific implementation manner of the embodiment of the disclosure, the training of the prediction model based on the type value and the classification prediction value of each image comprises
Constructing an objective function based on the type value and the predicted value;
training the predictive model based on the objective function.
According to a specific implementation manner of the embodiment of the present disclosure, after constructing the minimization objective function based on the type value and the classification prediction value of each image, the method further includes:
and carrying out multiple iterations on the neural network model g by utilizing the minimized objective function to obtain the minimum value of the minimized objective function.
According to a specific implementation manner of the embodiment of the present disclosure, the identifying the age of the target object in the newly acquired video file based on the trained prediction model includes:
determining a plurality of images to be identified containing target objects in the newly acquired video file;
predicting the image quality of the image to be recognized by using the trained prediction model to obtain a set of the image to be recognized containing a first type value;
and carrying out age identification on the image set to be identified containing the first type value by using the trained age regression neural network model.
In a second aspect, an embodiment of the present disclosure provides a target object age identification apparatus, including:
an acquisition module to acquire a plurality of images in a video file containing a target object, the target object having an edge perimeter on each of the plurality of images;
a determination module for determining a type value of an image containing a target object based on the edge perimeter;
the prediction module is used for predicting the classification of each image in the plurality of images through a prediction model to obtain a classification prediction value of each image;
the construction module is used for training the prediction model based on the type value and the classification predicted value of the image so as to enable the prediction precision of the prediction model to reach a preset value;
and the identification module is used for identifying the age of the target object in the newly acquired video file based on the trained prediction model.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, where the electronic device includes:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of identifying age of a target subject in any of the preceding aspects or any implementation manner of the first aspect.
In a fourth aspect, the disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the target object age identification method in the first aspect or any implementation manner of the first aspect.
In a fifth aspect, the present disclosure also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is caused to execute the target object age identification method in the foregoing first aspect or any implementation manner of the first aspect.
The target object age identification scheme in the disclosed embodiment comprises determining a type value of an image containing a target object based on the edge perimeter; predicting the classification of each image in the plurality of images through a prediction model to obtain a classification prediction value of each image; training the prediction model based on the type value and the classification predicted value of the image to enable the prediction precision of the prediction model to reach a preset value; and identifying the age of the target object in the newly acquired video file based on the trained prediction model. Through the processing scheme disclosed by the invention, the accuracy of the target object age prediction is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic view illustrating a process of identifying an age of a target object according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a neural network model provided in an embodiment of the present disclosure;
fig. 3 is a schematic view of another process for identifying the age of a target object according to an embodiment of the present disclosure;
fig. 4 is a schematic view of another process for identifying the age of a target object according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a target object age identification apparatus according to an embodiment of the present disclosure;
fig. 6 is a schematic diagram of an electronic device provided in an embodiment of the present disclosure.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and size of the components in actual implementation, and the type, amount and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
The embodiment of the disclosure provides a target object age identification method. The target object age identification method provided by the present embodiment may be executed by a computing device, which may be implemented as software, or implemented as a combination of software and hardware, and may be integrally provided in a server, a terminal device, or the like.
Referring to fig. 1, a method for identifying an age of a target object according to an embodiment of the present disclosure includes the following steps:
s101, a plurality of images containing a target object are obtained in a video file, and the target object has an edge perimeter on each of the plurality of images.
The target object is an object targeted at age recognition, and the target object may be all images capable of age recognition, for example, the target object may be all or part of an image of a person/animal/plant, and as an example, the target object may be a human face region.
The user usually likes to record a piece of video content containing the target object in the form of a video file, and by analyzing the age of the target object in the video file, a related video matched with the age of the target object can be pushed to the target object. For example, the target object in the video file is found to be a child through analysis, and at this time, the animation type video file which is liked by the child can be pushed to the user of the video file.
Before analyzing a target object, the video file needs to be split into a plurality of video frame images, and a video frame containing the target object is selected from the plurality of video frame images to form a plurality of images containing the target object. Taking a face region as an example, face detection can be performed on all video frames in the video file, and if the video frames contain the face region, the image is selected as the image containing the target object.
After the target object is detected, edge detection may be performed on the target object, and by the edge detection, an edge perimeter of the target area on each video frame image may be obtained.
S102, determining a type value of an image containing the target object based on the edge perimeter.
After the edge perimeter of the target object is obtained, the quality of the image where the target object is located can be determined based on the edge perimeter, and generally, the larger the edge perimeter of the target object is, the larger the image area of the target object itself is, and at this time, the sharper the image of the target object is. The clear target object image contains more image information, and the real age of the target object can be judged more accurately.
The type of the image containing the target object may be determined by setting a perimeter threshold, for example, the perimeter threshold may be 100 pixels. An image containing a target object equal to or greater than a perimeter threshold value is regarded as a first type of image (e.g., a high-quality image), and a first type value (e.g., 0x01) is set for the first type of image. An image containing the target object that is less than the perimeter threshold is considered to be a second type of image (e.g., a low quality image), and a second type value (e.g., 0x02) is set for the second type of image.
Taking the face region as an example, a face detection algorithm can be used to detect faces in various images. The low-quality face image can be obtained by selecting pixel points with the perimeter of the face region smaller than 160, the high-definition image is selected as the high-quality face image by the annotating personnel from the face image with the perimeter larger than the size, and the blurred face image is the low-quality face image.
S103, predicting the classification of each image in the plurality of images through a prediction model to obtain a classification prediction value of each image.
In order to be able to predict the quality of each of the plurality of images, a neural network model g is constructed, see fig. 2, comprising convolutional layers, pooling layers, sampling layers and fully-connected layers.
The convolutional layers mainly comprise the size of convolutional kernels and the number of input feature graphs, each convolutional layer can comprise a plurality of feature graphs with the same size, the feature values of the same layer adopt a weight sharing mode, and the sizes of the convolutional kernels in each layer are consistent. The convolution layer performs convolution calculation on the input image and extracts the layout characteristics of the input image.
The back of the feature extraction layer of the convolutional layer can be connected with the sampling layer, the sampling layer is used for solving the local average value of the input image and carrying out secondary feature extraction, and the sampling layer is connected with the convolutional layer, so that the neural network model can be guaranteed to have better robustness for the input image.
In order to accelerate the training speed of the neural network model g, a pooling layer is arranged behind the convolutional layer, the pooling layer processes the output result of the convolutional layer in a maximum pooling mode, and invariance characteristics of an input image can be better extracted.
The full-connection layer integrates the features in the image feature map passing through the plurality of convolution layers and the pooling layer, and obtains the quality features of the input image features for distinguishing image quality. In the neural network model g, the fully-connected layer maps the feature map generated by the convolutional layer into a fixed-length feature vector. The feature vector contains the combined information of all the features of the input image, and the feature vector reserves the image features with the most features in the image to complete the image classification task. In this way, the value of the specific category to which the input image belongs (the probability of the category to which the input image belongs) can be calculated, and the classification task can be completed by outputting the most possible category. For example, after calculation by the fully connected layer, the input image may be classified as a result containing a [ high quality, low quality ] class, with corresponding probabilities of [ P1, P2], respectively.
And S104, training the prediction model based on the type value and the classification predicted value of each image, so that the prediction precision of the prediction model reaches a preset value.
After the neural network model g is constructed, for any input image xi, a classification prediction result g (xi) can be obtained, and the accuracy of the neural network model g can be evaluated by comparing the difference value between the g (xi) and the image quality calibration value yi of the image xi.
Specifically, the minimum objective function f (x, y) | | g (xi) -yi | | | | | ^2 may be constructed on all training samples to train the neural network model g. The training process requires multiple iterations to find the minimum of the objective function.
And S105, identifying the age of the target object in the newly acquired video file based on the trained prediction model.
After the training of the prediction model is completed, the trained prediction model is used for predicting the quality of the target object image in the newly acquired video file aiming at the input of a given new video file containing the target object (such as a human face image) based on the prediction model. For example, if the prediction result is greater than 0.5, the target object image may be determined to be a high-quality target object image, and otherwise, the target object image may be determined to be a low-quality target object image.
After distinguishing the images in the newly acquired video file, age prediction may be performed on the high quality images in the newly acquired video file using an age recognition algorithm (e.g., an age regression neural network model) trained in advance. Thereby improving the accuracy of age identification.
Multiple images containing the target object can be acquired from the video file in multiple modes, and as one mode, the video file can be analyzed to obtain multiple video frame images. By means of object detection of the video frame images, an image containing the object can be selected from a plurality of video frames.
Referring to fig. 3, according to a specific implementation manner of the embodiment of the present disclosure, the determining a type value of an image including a target object based on the edge perimeter includes:
s301, setting a first type value for the image with the edge perimeter larger than a preset threshold value.
S302, setting a second type value for the image with the edge perimeter smaller than a preset threshold value.
Specifically, in the process of implementing steps S301 to S302, the type of the image including the target object may be determined by setting a perimeter threshold, for example, the perimeter threshold may be 100 pixels. An image containing a target object equal to or greater than a perimeter threshold value is regarded as a first type of image (e.g., a high-quality image), and a first type value (e.g., 0x01) is set for the first type of image. An image containing the target object that is less than the perimeter threshold is considered to be a second type of image (e.g., a low quality image), and a second type value (e.g., 0x02) is set for the second type of image.
Referring to fig. 4, according to a specific implementation manner of the embodiment of the present disclosure, the identifying an age of a target object in a newly acquired video file based on a trained prediction model includes:
s401, determining a plurality of images to be recognized containing target objects in the newly acquired video file.
And splitting the newly acquired video file into a plurality of video frame images, and selecting a video frame containing the target object from the plurality of video frame images to form a plurality of images containing the target object. Taking a face region as an example, face detection can be performed on all video frames in the video file, and if the video frames contain the face region, the image is selected as an image to be recognized, which contains the target object.
S402, predicting the image quality of the image to be recognized by using the trained prediction model to obtain an image set to be recognized containing the first type value.
After the training of the prediction model is completed, the trained prediction model is used for predicting the quality of the target object image in the newly acquired video file aiming at the input of a given new video file containing the target object (such as a human face image) based on the prediction model. For example, if the prediction result is greater than 0.5, it may be determined that the target object image is a high-quality target object image, i.e., a set of images to be recognized with a first type value, or else, it is determined that the target object image is a low-quality target object image, i.e., a set of images to be recognized with a second type value.
And S403, performing age identification on the image set to be identified containing the first type value by using the trained age regression neural network model.
After distinguishing the images in the newly acquired video file, age prediction may be performed on the high quality images in the newly acquired video file using an age recognition algorithm (e.g., an age regression neural network model) trained in advance. Thereby improving the accuracy of age identification.
Corresponding to the above method embodiment, referring to fig. 5, the present disclosure also discloses a target object age identifying apparatus 50, including:
an obtaining module 501 is configured to obtain a plurality of images in a video file, where the plurality of images include a target object, and the target object has an edge perimeter on each of the plurality of images.
The target object is an object targeted at age recognition, and the target object may be all images capable of age recognition, for example, the target object may be all or part of an image of a person/animal/plant, and as an example, the target object may be a human face region.
The user usually likes to record a piece of video content containing the target object in the form of a video file, and by analyzing the age of the target object in the video file, a related video matched with the age of the target object can be pushed to the target object. For example, the target object in the video file is found to be a child through analysis, and at this time, the animation type video file which is liked by the child can be pushed to the user of the video file.
Before analyzing a target object, the video file needs to be split into a plurality of video frame images, and a video frame containing the target object is selected from the plurality of video frame images to form a plurality of images containing the target object. Taking a face region as an example, face detection can be performed on all video frames in the video file, and if the video frames contain the face region, the image is selected as the image containing the target object.
After the target object is detected, edge detection may be performed on the target object, and by the edge detection, an edge perimeter of the target area on each video frame image may be obtained.
A determining module 502 for determining a type value of an image containing the target object based on the edge perimeter.
After the edge perimeter of the target object is obtained, the quality of the image where the target object is located can be determined based on the edge perimeter, and generally, the larger the edge perimeter of the target object is, the larger the image area of the target object itself is, and at this time, the sharper the image of the target object is. The clear target object image contains more image information, and the real age of the target object can be judged more accurately.
The type of the image containing the target object may be determined by setting a perimeter threshold, for example, the perimeter threshold may be 100 pixels. An image containing a target object equal to or greater than a perimeter threshold value is regarded as a first type of image (e.g., a high-quality image), and a first type value (e.g., 0x01) is set for the first type of image. An image containing the target object that is less than the perimeter threshold is considered to be a second type of image (e.g., a low quality image), and a second type value (e.g., 0x02) is set for the second type of image.
Taking the face region as an example, a face detection algorithm can be used to detect faces in various images. The low-quality face image can be obtained by selecting pixel points with the perimeter of the face region smaller than 160, the high-definition image is selected as the high-quality face image by the annotating personnel from the face image with the perimeter larger than the size, and the blurred face image is the low-quality face image.
The prediction module 503 is configured to predict the classification of each of the plurality of images through a prediction model to obtain a classification prediction value of each image.
In order to be able to predict the quality of each of the plurality of images, a neural network model g is constructed, see fig. 2, comprising convolutional layers, pooling layers, sampling layers and fully-connected layers.
The convolutional layers mainly comprise the size of convolutional kernels and the number of input feature graphs, each convolutional layer can comprise a plurality of feature graphs with the same size, the feature values of the same layer adopt a weight sharing mode, and the sizes of the convolutional kernels in each layer are consistent. The convolution layer performs convolution calculation on the input image and extracts the layout characteristics of the input image.
The back of the feature extraction layer of the convolutional layer can be connected with the sampling layer, the sampling layer is used for solving the local average value of the input image and carrying out secondary feature extraction, and the sampling layer is connected with the convolutional layer, so that the neural network model can be guaranteed to have better robustness for the input image.
In order to accelerate the training speed of the neural network model g, a pooling layer is arranged behind the convolutional layer, the pooling layer processes the output result of the convolutional layer in a maximum pooling mode, and invariance characteristics of an input image can be better extracted.
The full-connection layer integrates the features in the image feature map passing through the plurality of convolution layers and the pooling layer, and obtains the quality features of the input image features for distinguishing image quality. In the neural network model g, the fully-connected layer maps the feature map generated by the convolutional layer into a fixed-length feature vector. The feature vector contains the combined information of all the features of the input image, and the feature vector reserves the image features with the most features in the image to complete the image classification task. In this way, the value of the specific category to which the input image belongs (the probability of the category to which the input image belongs) can be calculated, and the classification task can be completed by outputting the most possible category. For example, after calculation by the fully connected layer, the input image may be classified as a result containing a [ high quality, low quality ] class, with corresponding probabilities of [ P1, P2], respectively.
And the construction module 504 is configured to train the prediction model based on the type value and the classification prediction value of each image, so that the prediction accuracy of the prediction model reaches a preset value.
After the neural network model g is constructed, for any input image xi, a classification prediction result g (xi) can be obtained, and the accuracy of the neural network model g can be evaluated by comparing the difference value between the image quality calibration values yi of g (xi) and xi.
Specifically, the minimum objective function f (x, y) | | g (xi) -yi | | | | | ^2 may be constructed on all training samples to train the neural network model g. The training process requires multiple iterations to find the minimum of the objective function.
And the identifying module 505 is configured to identify the age of the target object in the newly acquired video file based on the trained prediction model.
After the training of the prediction model is completed, the trained prediction model is used for predicting the quality of the target object image in the newly acquired video file aiming at the input of a given new video file containing the target object (such as a human face image) based on the prediction model. For example, if the prediction result is greater than 0.5, the target object image may be determined to be a high-quality target object image, and otherwise, the target object image may be determined to be a low-quality target object image.
After distinguishing the images in the newly acquired video file, age prediction may be performed on the high quality images in the newly acquired video file using an age recognition algorithm (e.g., an age regression neural network model) trained in advance. Thereby improving the accuracy of age identification.
The apparatus shown in fig. 5 may correspondingly execute the content in the above method embodiment, and details of the part not described in detail in this embodiment refer to the content described in the above method embodiment, which is not described again here.
Referring to fig. 6, an embodiment of the present disclosure also provides an electronic device 60, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of identifying age of a target subject in the above method embodiments.
The disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the foregoing method embodiments.
The disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the target object age identification method in the aforementioned method embodiments.
Referring now to FIG. 6, a schematic diagram of an electronic device 60 suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 6, the electronic device 60 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 60 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 60 to communicate with other devices wirelessly or by wire to exchange data. While the figures illustrate an electronic device 60 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising the at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects the internet protocol addresses from the at least two internet protocol addresses and returns the internet protocol addresses; receiving an internet protocol address returned by the node evaluation equipment; wherein the obtained internet protocol address indicates an edge node in the content distribution network.
Alternatively, the computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from the at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, the first retrieving unit may also be described as a "unit for retrieving at least two internet protocol addresses".
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.
The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present disclosure should be covered within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (11)
1. A method for identifying the age of a target object, comprising:
obtaining a plurality of images in a video file containing a target object, the target object having an edge perimeter on each of the plurality of images;
determining a type value of an image containing a target object based on the edge perimeter;
predicting the classification of each image in the plurality of images through a prediction model to obtain a classification prediction value of each image;
training the prediction model based on the type value and the classification predicted value of the image to enable the prediction precision of the prediction model to reach a preset value;
and identifying the age of the target object in the newly acquired video file based on the trained prediction model.
2. The method of claim 1, wherein obtaining a plurality of images containing a target object in a video file comprises:
analyzing the video file to obtain a plurality of video frames;
and selecting an image containing the target object from the plurality of video frames to form the plurality of images.
3. The method of claim 1, wherein determining a type value for an image containing a target object based on the edge perimeter comprises:
setting the image with the edge perimeter larger than a preset threshold value as a first type value;
and setting the image with the edge perimeter smaller than a preset threshold value as a second type value.
4. The method of claim 1, wherein the predicting the classification of each of the plurality of images by the prediction model to obtain a classification prediction value of each image comprises:
setting a neural network model g corresponding to the prediction model, wherein the neural network model g comprises a convolutional layer, a pooling layer and a sampling layer;
and generating a classification prediction value of the image by using the neural network model g.
5. The method of claim 4, wherein the generating the classification prediction value of the image using the neural network model g comprises:
and setting the number of the convolutional layers and the sampling layers in the neural network model g to be respectively more than 2, and performing pooling processing on the image by adopting a maximum pooling mode after the convolutional layers.
6. The method of claim 4, wherein training the predictive model based on the type value and the class prediction value for each image comprises
Constructing an objective function based on the type value and the predicted value;
training the predictive model based on the objective function.
7. The method of claim 6, wherein after constructing the minimization objective function based on the type value and the classification prediction value of each image, the method further comprises:
and carrying out multiple iterations on the neural network model g by utilizing the minimized objective function to obtain the minimum value of the minimized objective function.
8. The method of claim 1, wherein identifying the age of the target object in the newly acquired video file based on the trained predictive model comprises:
determining a plurality of images to be identified containing target objects in the newly acquired video file;
predicting the image quality of the image to be recognized by using the trained prediction model to obtain a set of the image to be recognized containing a first type value;
and carrying out age identification on the image set to be identified containing the first type value by using the trained age regression neural network model.
9. An apparatus for identifying an age of a target object, comprising:
an acquisition module to acquire a plurality of images in a video file containing a target object, the target object having an edge perimeter on each of the plurality of images;
a determination module for determining a type value of an image containing a target object based on the edge perimeter;
the prediction module is used for predicting the classification of each image in the plurality of images through a prediction model to obtain a classification prediction value of each image;
the construction module is used for training the prediction model based on the type value and the classification predicted value of the image so as to enable the prediction precision of the prediction model to reach a preset value;
and the identification module is used for identifying the age of the target object in the newly acquired video file based on the trained prediction model.
10. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of target object age identification of any one of claims 1-8.
11. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the target object age identification method of any one of claims 1-8.
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