CN111523468A - Human body key point identification method and device - Google Patents
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Abstract
The application discloses a method and a device for identifying key points of a human body, which relate to the technical field of image identification and specifically comprise the following steps: acquiring a target image of a key point of a human body to be identified; inputting a target image into a pre-trained human key point detection model to obtain a heat map of each human key point in the target image, wherein the pre-trained human key point detection model is obtained by training based on a sample image labeled with a true heat map of each human key point, the radial action range of the true heat map of each human key point is determined based on a scale parameter, and the scale parameter is determined according to the distance value of coordinates between the corresponding human key point and the rest human key points; according to the heat map of each human body key point in the target image, the coordinates of each human body key point are determined, and the accuracy of detecting the human body key points is effectively improved.
Description
Technical Field
The application relates to the field of image processing, in particular to the technical field of image recognition, and particularly relates to a method and a device for recognizing key points of a human body.
Background
In the current general training algorithm for the human body key point detection model, because of the complexity of actual service data, data generally needs to be enhanced, the generalization capability of the model is improved, and the accuracy of the model for identifying the human body key points is further improved. Generally, the common data enhancement methods are: firstly, horizontally turning over a picture by random probability, then taking a human body detection frame as a center, intercepting a human body region according to a fixed size, finally carrying out affine transformation on the intercepted region, rotating by a random angle, and finally reducing the mean value to carry out standardized network feeding for training.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a storage medium for identifying key points of a human body.
According to a first aspect, there is provided a human body key point identification method, the method comprising: acquiring a target image of a key point of a human body to be identified; inputting a target image into a pre-trained human key point detection model to obtain a heat map of each human key point in the target image, wherein the pre-trained human key point detection model is obtained by training based on a sample image labeled with a true heat map of each human key point, the radial action range of the true heat map of each human key point is determined based on a scale parameter, and the scale parameter is determined according to the distance value of coordinates between the corresponding human key point and the rest human key points; and determining the coordinates of each human body key point according to the heat map of each human body key point in the target image.
According to a second aspect, there is provided a human body keypoint identification device, comprising: the acquisition module is configured to acquire a target image of a key point of a human body to be identified; the training module is configured to input a target image into a pre-trained human key point detection model to obtain a heat map of each human key point in the target image, and the pre-trained human key point detection model is obtained by training based on a sample image labeled with a true-value heat map of each human key point, wherein the radial action range of the true-value heat map of each human key point is determined based on a scale parameter which is determined according to the distance value of coordinates between the human key point corresponding to the scale parameter and the rest of the human key points; and the determining module is configured to determine the coordinates of each human body key point according to the heat map of each human body key point in the target image.
According to a third aspect, there is provided an electronic device comprising one or more processors; a storage device having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the method of human keypoint identification as any of the embodiments of the first aspect.
According to a fourth aspect, there is provided a computer readable medium, having stored thereon a computer program which, when executed by a processor, implements the human keypoint identification method as in any of the embodiments of the first aspect.
The method comprises the steps of obtaining a target image of a key point of a human body to be identified; inputting the target image into a pre-trained human key point detection model to obtain a heat map of each human key point in the target image, training the pre-trained human key point detection model based on a sample image labeled with a true heat map of each human key point, wherein, the radial action range of the truth-value heat map of each human body key point is determined based on the scale parameter which is determined according to the distance value of the coordinate between the human body key point corresponding to the scale parameter and the other human body key points, namely, the scale parameters are distinguished according to the distance values, so that the situation that the scale parameters are set to be larger fixed values only according to actual experience and are not beneficial to model regression training is avoided, or simply setting the scale parameter to a small fixed value according to practical experience causes the error to become large, and the accuracy of the coordinates of each human body key point determined by the model according to the heat map of each human body key point in the target image is improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a human keypoint identification method according to the application;
FIG. 3 is a schematic diagram of an application scenario of the human keypoint identification method according to the application;
FIG. 4 is a flowchart of one embodiment of a method of determining a truncated human image annotated with a truth heat map for each human keypoint in accordance with the embodiment of the human keypoint identification method shown in FIG. 2;
FIG. 5 is a schematic diagram of one embodiment of a human keypoint identification device according to the present application;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing a server according to embodiments of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows an exemplary system architecture 100 to which embodiments of the human keypoint identification method of the present application may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various communication client applications, such as an image recognition-type application, an image processing-type application, and the like.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices with display screens, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as a plurality of software or software modules (for example to provide human body keypoint identification) or as a single software or software module. And is not particularly limited herein.
The server 105 may be a server providing various services, for example, to identify a target image (for example, a video frame including a human body image obtained by shooting) of a human body key point to be identified uploaded by the terminal devices 101, 102, and 103, input the target image to a human body key point detection model trained in advance, obtain a heat map of each human body key point in the target image, and determine a coordinate of each human body key point according to the heat map of each human body key point in the target image.
It should be noted that the human body key point identification method provided in the embodiment of the present application is generally executed by the server 105, and accordingly, the human body key point identification device is generally disposed in the server 105.
It should be noted that the local of the server 105 may also directly store the target image of the human key point to be identified, and the server 105 may directly extract the target image of the local human key point to be identified for human key point identification, in this case, the exemplary system architecture 100 may not include the terminal devices 101, 102, 103 and the network 104.
It should be further noted that human key point identification applications may also be installed in the terminal devices 101, 102, and 103, and the terminal devices 101, 102, and 103 may also perform human key point identification based on a target image of a human key point to be identified, at this time, the human key point identification method may also be executed by the terminal devices 101, 102, and 103, and accordingly, the human key point identification device may also be disposed in the terminal devices 101, 102, and 103. At this point, the exemplary system architecture 100 may also not include the server 105 and the network 104.
The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server is software, it may be implemented as a plurality of software or software modules (for example, to provide a human body key point identification service), or may be implemented as a single software or software module. And is not particularly limited herein.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 2 shows a schematic flow diagram 200 of an embodiment of a human keypoint identification method that can be applied to the present application. The human body key point identification method comprises the following steps:
In this embodiment, the executing body may obtain a target image of the key point of the human body to be recognized from the terminal device or a local preset database.
The target image of the human body key point to be identified may include various images, such as a video frame of a video, an image downloaded through a network, an image captured by an image capturing device, and the like.
Here, the target image of the human body key point to be recognized is an image including a human body region. Specifically, the executing subject may first obtain an initial image including a human body region, perform human body region detection on the initial image including the human body region, then extract the detected human body region from the initial image, and determine the extracted human body region as a target image of a human body key point to be identified.
The human body region detection may be a detection technology for detecting characteristic information of a human body region in the prior art or a future development technology, for example, a human body region detection model trained in advance is used to detect the human body region, and the application is not limited thereto.
Furthermore, it should be noted that the body key points are used to locate body key parts of the body, such as the head, neck, shoulders, hands, legs and/or feet; for any body key part, the required human body key points can be one or more in positioning the body key part. Under different scenes, the specific positions and the number of the key points of the human body can be different due to different key parts of the body to be positioned, and the application does not limit the specific positions and the number.
In this embodiment, the executing subject inputs the obtained target image including the human body region to a human body key point detection model trained in advance to obtain a heat map of each human body key point in the target image.
Wherein, the heat map of each human body key point is a probability distribution map of the possible positions of the human body key point.
Here, the human key point detection model trained in advance is trained based on a sample image labeled with a true-value heat map of each human key point.
The network structure of the pre-trained human key point detection model may include a variety of structures, and specifically, the pre-trained human key point detection model may include a feature extraction network for extracting image features and a heat map extraction network for generating a heat map based on the image features, the heat map extraction network may be implemented by a convolutional group, and the feature extraction network may include, but is not limited to, the following networks: LeNet, ResNet, MobileNet, etc.
It is noted that the radial extent of action of the truth heat map for each human keypoint is determined based on scale parameters.
Specifically, the truth-value heat map of each human body key point satisfies the gaussian probability distribution, which can be represented by the following heat map generation formula:
wherein,
x is the two-dimensional coordinate of the pixel point of the truth-value heat map of the human body key point, and mu is the truth-value two-dimensional coordinate of the human body key point and is a scale parameter.
From the above formula, the radial action range of the true-value heat map is determined based on the scale parameter, the larger the scale parameter is, the more gentle the gaussian distribution is, and the lower the central value is, otherwise, the smaller the scale parameter is, the steeper the gaussian distribution is, and the higher the central value is.
Typically, the scale parameter is a fixed value. The scale parameters cannot be set too large or too small, if the scale parameters are set too large, Gaussian distribution between two adjacent human key points is easy to overlap and interlace, the error becomes large, and if the scale parameters are set too small, the central value of the human key points is too large, so that the regression training of the model is not facilitated.
In this embodiment, the scale parameter is determined according to a distance value of coordinates between the human body key point corresponding to the scale parameter and the rest of the human body key points.
Specifically, the executing body may compare a minimum value of distance values of coordinates between the human key point corresponding to the scale parameter and the remaining human key points with a preset distance value, and if the minimum value is smaller than the preset distance value, that is, the human key point corresponding to the scale parameter is located in a region where the human key points are dense, for example, a human face region, the scale parameter may be set to a smaller value according to actual experience to reduce overlapping of gaussian distribution values between the human key points; if the minimum value is greater than or equal to the preset distance value, that is, the human key points corresponding to the scale parameters are located in regions where the human key points are sparse, such as shoulders, legs and the like, the scale parameters can be set to be larger values according to actual experience, so as to facilitate regression training of the model.
Here, the smaller value and the larger value may be fixed values, or may be values determined according to a minimum value among distance values of coordinates between the human body key point corresponding to the scale parameter and the remaining human body key points, which is not limited in the present application.
In addition, since the input image of the pre-trained human key point detection model is usually a fixed size, and the size of the target image of the human key point to be recognized is uncertain, the size of the target image may be adjusted to the size of the input image of the pre-trained human key point detection model before the target image is input to the pre-trained human key point detection model.
In some optional manners, the determining of the scale parameter according to the distance value of the coordinates between the corresponding human body key point and the rest human body key points includes: if the minimum value in the distance values of the coordinates between the human body key point corresponding to the scale parameter and the rest human body key points is larger than or equal to a preset distance value, the scale parameter is a preset fixed value; and if the minimum value in the distance values of the coordinates between the human body key point corresponding to the scale parameter and the rest human body key points is smaller than the preset distance value, determining the scale parameter according to the minimum value.
In this implementation manner, if the minimum value of the distance values of the coordinates between the human key point corresponding to the scale parameter and the rest of the human key points is greater than or equal to the preset distance value, that is, the human key point corresponding to the scale parameter is located in an area where the human key points are sparse, such as the shoulders, the legs, and the like, the scale parameter may be set to a larger preset fixed value so as to enable the model to quickly regress, thereby ensuring the training efficiency of the model.
If the minimum value in the distance values of the coordinates between the human key point corresponding to the scale parameter and the rest of the human key points is smaller than the preset distance value, that is, the human key point corresponding to the scale parameter is located in a region where the human key points are dense, such as a human face, the scale parameter can be determined according to the minimum value to avoid overlapping and interweaving of Gaussian distribution between two adjacent human key points, and the error becomes large, so that the accuracy of the human key point detection model on the identification of the human key points is influenced.
Specifically, the scale parameter may be proportional to the minimum value in the distance, that is, the scale parameter increases with the increase of the minimum value, and the proportional scaling factor may be determined according to practical experience and a specific application scenario.
The implementation mode compares the minimum value in the distance values of the coordinates between the human body key point corresponding to the scale parameter and the rest human body key points with a preset distance value, and sets the scale parameter of which the minimum value in the distance values of the coordinates between the human body key point corresponding to the scale parameter and the rest human body key points is more than or equal to the preset distance value as a fixed value; the scale parameter with the minimum value smaller than the preset distance value in the distance values of the coordinates between the human key points corresponding to the scale parameter and the rest of the human key points is determined according to the minimum value, so that the accuracy of human key point identification is guaranteed while the training efficiency of the human key point detection model is improved.
And step 203, determining the coordinates of each human body key point according to the heat map of each human body key point in the target image.
In this embodiment, after the execution subject obtains the heat map of each human body key point in the target image, the execution subject may determine the coordinates of each human body key point according to the heat map of each human body key point in the target image based on a preset identification manner.
The preset identification mode may include multiple types.
For example, the coordinates of each human body key point are determined from the heat map of each human body key point in the target image according to the following predetermined first calculation formula.
Wherein the predetermined first calculation formula may include:
Ix＝hx*s1；
Iy＝hy*s2；
here, Ix and Iy are respectively the abscissa and the ordinate of the human body key point I, and hx and hy are respectively the abscissa and the ordinate of the pixel point with the largest value in the heat map of the human body key point I; s1 is the reduction factor of the output content of the pre-trained human keypoint detection model with respect to the input content in the x-axis direction, and s2 is the reduction factor of the output content of the pre-trained human keypoint detection model with respect to the input content in the y-axis direction.
For another example, the coordinates of each human body key point are determined according to the formula modified by the following predetermined first calculation formula and the heat map of each human body key point in the target image, and the like, which is not limited in the present application.
The formula modified by the predetermined first calculation formula may include:
Ix＝hx*s1+R1；
Iy＝hy*s2+R2；
wherein, R1 and R2 are preset correction factors.
With continuing reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the human body keypoint identification method according to the present embodiment.
In the application scenario of fig. 3, an executing subject 301 acquires a target image 302 of a human body key point to be identified; inputting a target image 303 to a pre-trained human key point detection model 303 to obtain a heat map 304 of each human key point in the target image, wherein the pre-trained human key point detection model 303 is obtained by training based on a sample image labeled with a true heat map of each human key point, a radial action range of the true heat map of each human key point is determined based on a scale parameter, a minimum value in distance values of coordinates between the human key point corresponding to the scale parameter and the other human key points is compared with a preset distance value, and the scale parameter is determined according to a comparison result; finally, the coordinates 305 of each human body key point are determined according to the heat map 304 of each human body key point in the target image.
According to the method for identifying the human key points, the target images of the human key points to be identified are obtained; inputting a target image into a pre-trained human key point detection model to obtain a heat map of each human key point in the target image, wherein the pre-trained human key point detection model is obtained by training based on a sample image labeled with a true heat map of each human key point, the radial action range of the true heat map of each human key point is determined based on a scale parameter, and the scale parameter is determined according to the distance value of coordinates between the corresponding human key point and the rest human key points; according to the heat map of each human body key point in the target image, the coordinates of each human body key point are determined, and the accuracy of detecting the human body key points is effectively improved.
With further reference to FIG. 4, a flow 400 of one embodiment of a method of determining a truncated human image annotated with a true-value heat map for each human keypoint in the embodiment of the human keypoint identification method shown in FIG. 2 is shown. Here, the truncated human body image labeled with the true-value heat map for each human body keypoint is used to train the previously trained human body keypoint detection model in fig. 2 described above. In this embodiment, the process 400 of obtaining the truncated human body image marked with the true-value heat map of each human body key point may include the following steps:
In this embodiment, the execution subject may obtain an original image from the terminal device or the local preset database, where the original image includes a human body image labeled with coordinates of each human body key point and a human body detection frame.
The human body detection frame is used for marking the human body image in the original image. Usually, each human body detection frame includes a complete human body image.
In this embodiment, the first preset size is determined according to the size of the human body detection frame, wherein the size of the human body detection frame may be determined according to actual experience and a specific application scenario. Specifically, the human body detection frame may be square or rectangular.
In some optional manners, before the original image is captured an image of a first preset size with a center of the human detection frame as a center and a human detection frame as a boundary, and a first image is obtained, the method further includes: and rotating the human body detection frame by random probability and random angle.
In this implementation manner, before the execution main body intercepts the human body detection frame of the original image, the execution main body may further perform random probability and random angle rotation on the human body detection frame to further perform data enhancement on image data in the human body detection frame, thereby further enhancing the generalization capability of the human body key point detection model.
And 403, expanding the human body detection frame in the first image by a second preset size to obtain a second image.
In this embodiment, the executing subject expands a second preset size outside the human body detection frame in the first image and fills the second preset size with 0 pixel value to obtain a second image.
The second preset size may be determined according to actual experience and a specific application scenario, and preferably, the area of the image region corresponding to the second preset size may be greater than or equal to the area of the image region corresponding to the size of the human body detection frame in the first image, so as to ensure a better truncation effect. For example, the size of the human body detection frame in the first image is d × d, and after the length and width of the human body detection frame are respectively extended by 50%, the size of the second image is 1.5d × 1.5 d.
And 404, randomly intercepting the image with the first preset size from the second image to obtain an image of the truncated human body marked with the coordinates of each key point of the human body.
In this embodiment, after the execution subject obtains the second image, the execution subject randomly intercepts an image of a first preset size to obtain an image of a truncated human body labeled with coordinates of each key point of the human body.
Here, after acquiring the image of the truncated human body to which the coordinates of each human body key point are labeled, the executing body may further add gaussian noise or salt and pepper noise of random size at random for further data enhancement.
And 405, obtaining an image of the truncated human body, which is marked with a true-value heat map of each human body key point, according to the image of the truncated human body, which is marked with the coordinates of each human body key point.
In this embodiment, after the executing entity obtains the image of the truncated human body labeled with the coordinates of each human body key point, the executing entity may first perform coordinate transformation on the coordinates of each human body key point in the image according to the transformation to obtain the true value coordinates of each human body key point, and then obtain the image of the truncated human body labeled with the true value heat map of each human body key point according to the true value coordinates of each human body key point.
The truth-value heat map of each human body key point satisfies the Gaussian probability distribution, and can be represented by the heat map generation formula, which is not described herein again.
In some optional modes, the number of the human body key points of the truncated human body image marked with the true-value heat map of each human body key point is greater than or equal to a preset number value.
In this implementation manner, the execution main body limits the number of human key points of the truncated human body image labeled with the true-value heat map of each human key point, so that the influence on the accuracy of the human key point detection by the human key point detection model due to the too small number of human key points of the truncated human body image labeled with the true-value heat map of each human key point can be effectively avoided.
The above embodiment of the present application mainly describes an acquisition process of an image of a truncated human body, in which a true-value heat map of each human body key point is labeled in the embodiment of the human body key point identification method shown in fig. 2. Here, the truncated human image labeled with the true-value heat map for each human keypoint is used to train the pre-trained human keypoint detection model in fig. 2. According to the embodiment, the training data of the human body cut-off scene is increased by acquiring the cut-off human body image marked with the true-value heat map of each human body key point, and the acquired cut-off human body image marked with the true-value heat map of each human body key point is trained on the human body key point detection model trained in advance, so that the detection precision of the human body cut-off scene by the human body key point detection model trained in advance is improved, and the accuracy of detecting the human body key points is effectively improved.
With further reference to fig. 5, as an implementation of the methods shown in the above-mentioned figures, the present application provides an embodiment of a human body key point identification apparatus, which corresponds to the method embodiment shown in fig. 1, and which can be applied to various electronic devices.
As shown in fig. 5, the human body key point recognition apparatus 500 of the present embodiment includes: an acquisition module 501, a training module 502, and a determination module 503.
The obtaining module 501 may be configured to obtain a target image of a key point of a human body to be identified.
The training module 502 may be configured to input the target image into a pre-trained human key point detection model, to obtain a heat map of each human key point in the target image, where the pre-trained human key point detection model is obtained by training based on a sample image labeled with a true-value heat map of each human key point, where a radial acting range of the true-value heat map of each human key point is determined based on a scale parameter determined according to a distance value of a coordinate between a human key point corresponding to the scale parameter and the rest of the human key points.
The obtaining module 503 may be configured to determine coordinates of each human body key point according to a heat map of each human body key point in the target image.
In some optional manners of this embodiment, the determining, by the scale parameter, the distance value of the coordinate between the corresponding human body key point and the remaining human body key points includes: if the minimum value in the distance values of the coordinates between the human body key point corresponding to the scale parameter and the rest human body key points is larger than or equal to a preset distance value, the scale parameter is a preset fixed value; and if the minimum value in the distance values of the coordinates between the human body key point corresponding to the scale parameter and the rest human body key points is smaller than the preset distance value, determining the scale parameter according to the minimum value.
In some optional manners of this embodiment, the sample image labeled with the true-value heat map of each human body key point includes: the image of the truncated human body is marked with a true-value heat map of each human body key point; and the truncated human body image marked with the true-value heat map of each human body key point is obtained by the following method: acquiring an original image, wherein the original image comprises a human body image marked with coordinates of each human body key point and a human body detection frame; in the original image, intercepting an image with a first preset size by taking the center of a human body detection frame as the center and taking the human body detection frame as the boundary to obtain a first image; expanding the human body detection frame in the first image by a second preset size to obtain a second image; randomly intercepting an image with a first preset size from the second image to obtain an image of a truncated human body marked with coordinates of each human body key point; and obtaining the image of the truncated human body marked with the true-value heat map of each human body key point according to the image of the truncated human body marked with the coordinate of each human body key point.
In some optional manners of this embodiment, the number of the human body key points of the truncated human body image labeled with the true-value heat map of each human body key point is greater than or equal to a preset number value.
In some optional manners of this embodiment, before capturing, in the original image, an image of a first preset size with a center of the human body detection frame as a center and a human body detection frame as a boundary, and obtaining a first image, the method further includes: and rotating the human body detection frame by random probability and random angle.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
As shown in fig. 6, the embodiment of the present application is a block diagram of an electronic device for a human body key point identification method.
600 is a block diagram of an electronic device for a human keypoint identification method according to an embodiment of the application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 6, the electronic apparatus includes: one or more processors 601, memory 602, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 6, one processor 601 is taken as an example.
The memory 602 is a non-transitory computer readable storage medium as provided herein. The memory stores instructions executable by at least one processor to cause the at least one processor to perform the human keypoint identification method provided by the application. The non-transitory computer-readable storage medium of the present application stores computer instructions for causing a computer to perform the human keypoint identification method provided by the present application.
The memory 602, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules (e.g., the obtaining module 501, the training module 502, and the determining module 503 shown in fig. 5) corresponding to the human keypoint identification method in the embodiment of the present application. The processor 601 executes various functional applications of the server and data processing by running non-transitory software programs, instructions and modules stored in the memory 602, that is, implementing the human body key point identification method in the above method embodiments.
The memory 602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by use of the electronic device for face tracking, and the like. Further, the memory 602 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 602 optionally includes memory remotely located from the processor 601, and these remote memories may be connected over a network to the human keypoint identification electronics. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the human body key point identification method may further include: an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603 and the output device 604 may be connected by a bus or other means, and fig. 6 illustrates the connection by a bus as an example.
The input device 603 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic device for quality monitoring of the live video stream, such as a touch screen, keypad, mouse, track pad, touch pad, pointer stick, one or more mouse buttons, track ball, joystick, or like input device. The output devices 604 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the application, the accuracy of detecting the key points of the human body is effectively improved.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (12)
1. A human body key point identification method comprises the following steps:
acquiring a target image of a key point of a human body to be identified;
inputting the target image into a pre-trained human key point detection model to obtain a heat map of each human key point in the target image, wherein the pre-trained human key point detection model is obtained by training based on a sample image labeled with a true heat map of each human key point, the radial action range of the true heat map of each human key point is determined based on a scale parameter, and the scale parameter is determined according to the distance value of coordinates between the human key point corresponding to the scale parameter and the rest human key points;
and determining the coordinates of each human body key point according to the heat map of each human body key point in the target image.
2. The method of claim 1, wherein the determining of the scale parameter according to the distance value of the coordinates between the corresponding human key point and the remaining human key points comprises:
if the minimum value in the distance values of the coordinates between the human body key point corresponding to the scale parameter and the rest human body key points is larger than or equal to a preset distance value, the scale parameter is a preset fixed value;
and if the minimum value in the distance values of the coordinates between the human body key point corresponding to the scale parameter and the rest human body key points is smaller than a preset distance value, determining the scale parameter according to the minimum value.
3. The method of claim 1, wherein the sample image annotated with a truth heat map for each human keypoint comprises: the image of the truncated human body is marked with a true-value heat map of each human body key point; and the truncated human body image marked with the true-value heat map of each human body key point is obtained by the following method:
acquiring an original image, wherein the original image comprises a human body image marked with coordinates of each human body key point and a human body detection frame;
in the original image, intercepting an image with a first preset size by taking the center of a human body detection frame as the center and taking the human body detection frame as the boundary to obtain a first image;
expanding the human body detection frame in the first image by a second preset size to obtain a second image;
randomly intercepting an image with a first preset size from the second image to obtain an image of a truncated human body marked with coordinates of each human body key point;
and obtaining the image of the truncated human body marked with the true-value heat map of each human body key point according to the image of the truncated human body marked with the coordinates of each human body key point.
4. The method according to claim 3, wherein the number of human body key points of the image of the truncated human body marked with the true-value heat map of each human body key point is greater than or equal to a preset number value.
5. The method according to claim 3, wherein before the step of capturing the first image in the original image, the first image being obtained by taking the center of the human body detection frame as the center and the human body detection frame as the boundary, the method further comprises:
and rotating the human body detection frame by random probability and random angle.
6. A human keypoint identification device comprising:
the acquisition module is configured to acquire a target image of a key point of a human body to be identified;
a training module configured to input the target image into a pre-trained human key point detection model to obtain a heat map of each human key point in the target image, wherein the pre-trained human key point detection model is obtained by training based on a sample image labeled with a true-value heat map of each human key point, a radial acting range of the true-value heat map of each human key point is determined based on a scale parameter, and the scale parameter is determined according to a distance value of coordinates between a human key point corresponding to the scale parameter and the rest of human key points;
a determining module configured to determine coordinates of each human body key point according to a heat map of each human body key point in the target image.
7. The apparatus of claim 6, wherein the determining of the scale parameter according to the distance value of the coordinates between the corresponding human key point and the remaining human key points comprises:
if the minimum value in the distance values of the coordinates between the human body key point corresponding to the scale parameter and the rest human body key points is larger than or equal to a preset distance value, the scale parameter is a preset fixed value;
and if the minimum value in the distance values of the coordinates between the human body key point corresponding to the scale parameter and the rest human body key points is smaller than a preset distance value, determining the scale parameter according to the minimum value.
8. The apparatus of claim 6, wherein the sample image annotated with a truth heat map for each human keypoint comprises: the image of the truncated human body is marked with a true-value heat map of each human body key point; and the truncated human body image marked with the true-value heat map of each human body key point is obtained by the following method:
acquiring an original image, wherein the original image comprises a human body image marked with coordinates of each human body key point and a human body detection frame;
in the original image, intercepting an image with a first preset size by taking the center of a human body detection frame as the center and taking the human body detection frame as the boundary to obtain a first image;
expanding the human body detection frame in the first image by a second preset size to obtain a second image;
randomly intercepting an image with a first preset size from the second image to obtain an image of a truncated human body marked with coordinates of each human body key point;
and obtaining the image of the truncated human body marked with the true-value heat map of each human body key point according to the image of the truncated human body marked with the coordinates of each human body key point.
9. The apparatus of claim 8, wherein the number of human body key points of the image of the truncated human body, to which the true-value heat map of each human body key point is labeled, is greater than or equal to a preset number value.
10. The apparatus of claim 8, wherein before the capturing an image of a first preset size with a center of a human detection frame as a center and a human detection frame as a boundary in the original image to obtain a first image, the method further comprises:
and rotating the human body detection frame by random probability and random angle.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory is stored with instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-5.
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