Attitude reference data acquisition method and device, electronic equipment and medium

Abstract

The embodiment of the invention provides an attitude reference data acquisition method and device, electronic equipment and a medium, and the method comprises the steps: obtaining attitude data, corresponding to a first image, of an object according to the first image; according to the second image, obtaining attitude data of the object corresponding to the second image; obtaining a first probability density function according to the attitude data corresponding to the first image and the attitude data corresponding to the second image; according to the first probability density function, first attitude reference data of the object are obtained, and the first attitude reference data are attitude data when the object is in the first reference motion state. The attitude reference data of the object can be obtained.

Description

Technical Field

[0001] The present application relates to the technical field of electronic equipment, and in particular to a method, device, electronic equipment and medium for acquiring attitude reference data. Background Art

[0002] The posture of an object (such as a human being) can reflect the object's motion state. In order to analyze and process the object's motion state, it is necessary to obtain the object's posture reference data. Summary of the invention

[0003] The embodiments of the present application provide a method, device, electronic device and medium for acquiring attitude reference data, which can acquire attitude reference data of an object.

[0004] In a first aspect, an embodiment of the present application provides a method for acquiring posture reference data, comprising: acquiring posture data of an object corresponding to the first image based on a first image; acquiring posture data of an object corresponding to the second image based on a second image; obtaining a first probability density function based on the posture data corresponding to the first image and the posture data corresponding to the second image; acquiring first posture reference data of the object based on the first probability density function, the first posture reference data being posture data of the object when it is in a first reference motion state.

[0005] Optionally, the step of obtaining a first probability density function according to posture data corresponding to the first image and posture data corresponding to the second image includes: obtaining a first kernel density function according to the posture data corresponding to the first image; obtaining a second kernel density function according to the posture data corresponding to the second image; obtaining the first probability density function according to the first kernel density function and the second kernel density function.

[0006] Optionally, the posture reference data acquisition method also includes: when it is determined that the number of posture data in the first posture data set does not reach a preset quantity threshold, the posture data corresponding to the first image is added to the first posture data set to obtain a second posture data set; after obtaining the second posture data set, the step of obtaining the first kernel density function is executed; after obtaining the first kernel density function, whether the number of posture data in the second posture data set reaches the quantity threshold; when it is determined that the number of posture data in the second posture data set does not reach the quantity threshold, the posture data corresponding to the second image is added to the second posture data set to obtain a third posture data set; after obtaining the third posture data set, the step of obtaining the second kernel density function is executed; after obtaining the second kernel density function, whether the number of posture data in the third posture data set reaches the quantity threshold; when it is determined that the number of posture data in the third posture data set reaches the quantity threshold, the step of obtaining the first probability density function is executed.

[0007] Optionally, the posture data includes a first posture angle; the posture reference data acquisition method also includes: obtaining the variance of the first posture angle according to the first posture angle in the posture data corresponding to the first image and the first posture angle in the posture data corresponding to the second image; when the obtained variance meets the preset requirements, determining that the first posture reference data is available, or when the obtained variance meets the preset requirements, executing the step of obtaining a first probability density function; wherein the preset requirements include that the variance of the obtained first posture angle is not greater than a preset variance threshold of the first posture angle.

[0008] Optionally, the posture reference data acquisition method further includes: analyzing the motion state of the object according to the first posture reference data.

[0009] Optionally, the first image and the second image are both images captured by an image acquisition device; the step of analyzing the motion state of the object based on the first posture reference data includes: obtaining a target image captured by the image acquisition device in real time; obtaining posture data of the object corresponding to the target image based on the target image; obtaining an analysis result based on the first posture reference data and the posture data corresponding to the target image, and the analysis result is used to reflect whether the object is in a motion state corresponding to the first posture reference data.

[0010] Optionally, the first image and the second image are both images captured by the image acquisition device within a first time period; the posture reference data acquisition method also includes: acquiring posture data of the object corresponding to the third image based on the third image captured by the image acquisition device within the second time period; acquiring posture data of the object corresponding to the fourth image based on the fourth image captured by the image acquisition device within the second time period; obtaining a second probability density function based on the posture data corresponding to the third image and the posture data corresponding to the fourth image; acquiring second posture reference data of the object based on the second probability density function, the second posture reference data being the posture data when the object is in a second reference motion state; analyzing the motion state of the object within the second time period based on the first posture reference data; analyzing the motion state of the object within the third time period based on the second posture reference data; wherein the first time period, the second time period and the third time period are any three time periods that exist adjacent to each other in chronological order.

[0011] In a second aspect, an embodiment of the present application provides a posture reference data acquisition device, comprising: a first acquisition module, used to acquire the posture data of the object corresponding to the first image based on the first image; acquire the posture data of the object corresponding to the second image based on the second image; a second acquisition module, used to obtain a first probability density function based on the posture data corresponding to the first image and the posture data corresponding to the second image; a third acquisition module, used to acquire first posture reference data of the object based on the first probability density function, the first posture reference data being the posture data of the object when it is in a first reference motion state.

[0012] In a third aspect, an embodiment of the present application provides an electronic chip, comprising: a processor, which is used to execute computer program instructions stored in a memory, wherein when the computer program instructions are executed by the processor, the electronic chip is triggered to execute any method as in the first aspect.

[0013] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising a memory for storing computer program instructions, a processor for executing the computer program instructions, and a communication device, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to execute a method as described in any one of the first aspects.

[0014] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer executes any method in the first aspect.

[0015] In a sixth aspect, an embodiment of the present application provides a computer program product, which includes a computer program. When the computer program runs on a computer, it enables the computer to execute any method in the first aspect.

[0016] The embodiment of the present application can obtain the posture reference data of the object. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flowchart of a method for acquiring attitude reference data provided by an embodiment of the present application;

[0019] Figure 2 A schematic diagram of fitting a probability distribution using a histogram is provided for one embodiment of the present application;

[0020] Figure 3 A schematic diagram of fitting a probability distribution using a kernel density function provided in one embodiment of the present application;

[0021] Figure 4a A schematic diagram of a successful calibration example corresponding to a pitch angle provided in an embodiment of the present application;

[0022] Figure 4b A schematic diagram of a successful calibration example corresponding to a yaw angle provided in an embodiment of the present application;

[0023] Figure 5a A schematic diagram of a calibration failure example corresponding to a pitch angle provided by an embodiment of the present application;

[0024] Figure 5b A schematic diagram of a calibration failure example corresponding to a yaw angle provided in an embodiment of the present application;

[0025] Figure 6a A schematic diagram of a reference vector estimation result corresponding to a pitch angle provided by an embodiment of the present application;

[0026] Figure 6b A schematic diagram of a reference vector estimation result corresponding to a yaw angle provided by an embodiment of the present application;

[0027] Figure 6c A schematic diagram of a reference vector estimation result corresponding to a roll angle provided by an embodiment of the present application;

[0028] Figure 7 A schematic diagram of a flow chart of another method for acquiring attitude reference data provided by an embodiment of the present application;

[0029] Figure 8 A block diagram of a posture reference data acquisition device provided by one embodiment of the present application;

[0030] Fig. 9 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0032] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0033] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0034] It should be understood that the term "at least one" used in this article refers to one or more, and "plurality" refers to two or more. The term "and / or" used in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. Among them, A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b and c can be represented by: a, b, c, ab, ac, bc, or abc, wherein a, b, c can be single or multiple.

[0035] It should be understood that, although the terms first, second, etc. may be used to describe the set thresholds in the embodiments of the present application, these set thresholds should not be limited to these terms. These terms are only used to distinguish the set thresholds from each other. For example, without departing from the scope of the embodiments of the present application, the first set threshold may also be referred to as the second set threshold, and similarly, the second set threshold may also be referred to as the first set threshold.

[0036] The terms used in the implementation section of this application are only used to explain the specific embodiments of this application and are not intended to limit this application.

[0037] like Figure 1 As shown, an embodiment of the present application provides a method for acquiring attitude reference data, which may include the following steps 101 to 111.

[0038] In one embodiment, before step 101, an initialization parameter setting operation may be performed first, and the initialization parameter setting operation may include constructing a queue, setting a quantity threshold, initializing a variance threshold, and configuring parameters of a probability density function.

[0039] In one embodiment, when constructing a queue, a pitch angle queue (e.g., queue input_pitch), a yaw angle queue (e.g., queue input_yaw), and a roll angle queue (e.g., queue input_roll) may be constructed respectively. Taking the pitch angle as an example, the pitch angle queue may be a list of pitch angles corresponding to multiple frames of images collected continuously.

[0040] When the queue contains posture angles with a preset number threshold, the queue can be used as the input of the head posture reference vector estimation algorithm, and combined with the kernel density estimation algorithm, the probability density function can be solved. The probability density function can reflect the probability density distribution of the posture angle. Among them, based on the kernel density estimation algorithm, the kernel density function of the posture angle can be obtained, and then the probability density function can be obtained according to the kernel density function of each posture angle in the queue.

[0041] In one embodiment, the quantity threshold may be 1500 (eg, size=1500), so that each image periodically captured by the camera within a corresponding time period (eg, 5 minutes) needs to be acquired. For example, 1500 frames of images may be sequentially extracted from the video captured by the camera.

[0042] In one embodiment, by initializing the variance threshold, the variance threshold corresponding to various attitude angle data sequences can be set. Taking the pitch angle as an example, the pitch angle data sequence can be each pitch angle in the pitch angle queue.

[0043] For example, a variance threshold of a pitch angle data sequence (for example, denoted as T_var_pitch), a variance threshold of a yaw angle data sequence (for example, denoted as T_var_yaw), and a variance threshold of a roll angle data sequence (for example, denoted as T_var_roll) may be set.

[0044] The set variance threshold can be used to determine whether the attitude reference data that can be obtained is appropriate. If it is appropriate, it can be considered that the attitude reference data is calibrated successfully (or the calibration is passed).

[0045] In one embodiment, if the reference pitch angle, reference yaw angle and reference roll angle in the attitude reference data are all calibrated successfully, the attitude reference data can be considered to be calibrated successfully. The attitude reference data can be used as a judgment reference to analyze and judge the motion behavior.

[0046] In one embodiment, the parameters of the probability density function may be configured: probability density distribution y_array(181, 1), scale x_array(181, 1). The Chinese interpretation of array may be array.

[0047] In one embodiment, "181, 1" may represent 181 rows and 1 column of data, where "181" is the dimension.

[0048] In one embodiment, the initialization parameter setting operation can be performed by configuring the contents of the following Table 1. Subsequently, based on the parameter values ​​set in Table 1, the attitude reference vector is estimated.

[0049] Table 1

[0050]

[0051] Step 101, obtaining an image captured in real time by a camera for capturing an image of the driver's head.

[0052] In one embodiment, a camera may be used to collect video data of a person's head. The image acquired in step 101 may be a frame of image extracted from a video (the video includes multiple consecutive frames of images), and the image is a 2D (two-dimensional) image. Any posture angle of the user's head may be obtained through the user's head image in the image.

[0053] In 3D space, the rotation of an object can be represented by three Euler angles: pitch around the X-axis, yaw around the Y-axis, and roll around the Z-axis. These three attitude angles, or the angles of raising the head, shaking the head, and turning the head, can be respectively used.

[0054] Step 102, obtaining the posture data of the driver's head according to the acquired image, the posture data including three posture angles: pitch angle, yaw angle, and roll angle.

[0055] In one embodiment, the head pose estimation (Head Pose Estimation) algorithm can be used to perform head pose estimation based on the image captured by the camera to estimate the head pose vector value including the three pose angles of pitch, yaw and roll, for example, recorded as headpose (pitch, yaw, roll).

[0056] Specifically, the posture angle of the head can be obtained through the 2D image.

[0057] It is feasible to obtain the attitude angle of the driver's head based on a frame of image in the video through a deep learning model.

[0058] Step 103, adding the pitch angle in the attitude data to a pitch angle queue, adding the yaw angle in the attitude data to a yaw angle queue, and adding the roll angle in the attitude data to a roll angle queue.

[0059] In one embodiment, the head posture vector value headpose (pitch, yaw, roll) may be added to the queue input_pitch, the queue input_yaw, and the queue input_roll respectively.

[0060] Step 104, according to the pitch angle in the attitude data, obtain the kernel density function corresponding to the pitch angle, according to the yaw angle in the attitude data, obtain the kernel density function corresponding to the yaw angle, according to the roll angle in the attitude data, obtain the kernel density function corresponding to the roll angle.

[0061] Kernel Density Estimation (KDE) is used in probability theory to estimate unknown density functions and is one of the non-parametric test methods. Based on the kernel density estimation technique, the kernel density function of a sample point can be obtained.

[0062] In one embodiment, the kernel density function or kernel function may be a Gaussian function.

[0063] By generating the corresponding kernel density function for the attitude angle newly added to the queue, the probability density distribution of the attitude angle can be updated in real time.

[0064] In one embodiment, the kernel density function can be obtained by the following formula (1).

[0065]

[0066] Among them, K(.) is the kernel density function, and a Gaussian kernel can be selected. x is any sample point in the sample set (x1, x2, ..., xn), for example, it can be a posture angle, and each x is independent and identically distributed.

[0067] Step 105, determine whether the number of attitude angles in the queue reaches a quantity threshold, if so, execute step 106, otherwise execute step 101.

[0068] In one embodiment, since the number of attitude angles in different queues is usually consistent, it can be determined whether the number of attitude angles in any queue reaches a number threshold. For example, it can be determined whether the number of attitude angles in the queue input_pitch, the queue input_yaw, or the queue input_roll reaches 1500.

[0069] In another embodiment, it may also be determined whether the number of attitude angles in each queue reaches a quantity threshold.

[0070] Step 106, obtain the probability density function of the corresponding pitch angle based on the kernel density function of the pitch angle in the pitch angle queue, obtain the probability density function of the corresponding yaw angle based on the kernel density function of the yaw angle in the yaw angle queue, and obtain the probability density function of the corresponding roll angle based on the kernel density function of the roll angle in the roll angle queue.

[0071] In one embodiment, taking the pitch angle as an example, the kernel density functions of the pitch angles may be superimposed to obtain the probability density function of the corresponding pitch angle.

[0072] Assuming that a sample set has N sample points (for example, there are N pitch angles in a pitch angle queue), after fitting the kernel density function of the N sample points, the corresponding N kernel density functions can be superimposed to obtain the probability density function of the entire sample set.

[0073] In one embodiment, assuming that the sample set (x1, x2, ..., xn) is sampled from an unknown probability distribution f, and each x is independent and identically distributed, the probability density function can be obtained by the following formula (2).

[0074]

[0075] Among them, f is the probability density function, K(.) is the kernel density function, and a Gaussian kernel can be selected.

[0076] Wherein, h is the bandwidth (band_width), that is, the standard deviation of the Gaussian kernel (the standard deviation of the Gaussian kernel is not the standard deviation of the sample data). In one embodiment, in order to simplify the calculation, h=1.0 can be taken.

[0077] In order to reflect the probability density distribution of the attitude angle, in one implementation, the probability distribution can be fitted by a histogram, and in another implementation, the following can be used: Figure 1 The embodiment described above adopts a kernel density function to fit the probability distribution.

[0078] Assume that a sample set is X = {x1 = -2.1, x2 = -1.3, x3 = -0.4, x4 = 1.9, x5 = 5.1, x6 = 6.2}, and the sample set has 6 sample points. When the probability distribution is fitted by the above two implementation methods, the schematic diagrams of the fitted probability distribution can be shown as follows: Figure 2 and Figure 3 As shown. Among them, Figure 2 A schematic diagram of fitting a probability distribution using a histogram is provided for one embodiment of the present application. Figure 3 A schematic diagram of using a kernel density function to fit a probability distribution is provided for one embodiment of the present application. Figure 2 and Figure 3 The horizontal axis is x, and the vertical axis can be the density function.

[0079] Please refer to Figure 3 , Figure 3 The six kernel density functions corresponding to the six sample points are shown in Figure 3 The dashed curve in the middle shows the probability density function obtained by superimposing 6 kernel density functions (as shown in Figure 3 (shown by the solid curve in the middle).

[0080] Combination Figure 2 and Figure 3 Compared with the implementation method of fitting probability distribution by histogram, Figure 1The illustrated embodiment combines the kernel density function and the probability density function to represent the probability density distribution of the attitude angle, which may have the following advantages:

[0081] 1) If Figure 3 As shown in , when the kernel density function is used to fit the probability distribution, a continuous and accurate probability distribution can be obtained. Figure 2 As shown in FIG. 1 , when a probability distribution is fitted by a histogram, the shape of the histogram depends largely on the number of selected bins, and the histogram statistics may have 0 bins, and the entire distribution is discontinuous.

[0082] 2) Although the head posture angle estimated by the head posture estimation algorithm for each frame may not be absolutely accurate, by converting each estimated value (such as the head posture angle) into a Gaussian function representation, the estimated probability density function can have higher robustness and accuracy.

[0083] Step 107, take the pitch angle corresponding to the maximum value in the probability density function of the pitch angle as the reference pitch angle, take the yaw angle corresponding to the maximum value in the probability density function of the yaw angle as the reference yaw angle, take the roll angle corresponding to the maximum value in the probability density function of the roll angle as the reference roll angle.

[0084] The probability density function can reflect the probability density distribution of the attitude angle, so the attitude angle with the maximum probability can be taken as the reference value of the attitude angle.

[0085] Step 108, calculating the variance of the pitch angle in the pitch angle queue, the variance of the yaw angle in the yaw angle queue, and the variance of the roll angle in the roll angle queue.

[0086] In one embodiment, taking the pitch angle as an example, the variance of 1500 pitch angles in the pitch angle queue may be calculated.

[0087] Figure 1 In the illustrated embodiment, a reference value is generated first, and then the validity of the generated reference value is calibrated according to the variance. In other embodiments, the validity of the generated reference value can also be calibrated according to the variance (i.e., the validity of the reference value is predicted). If the calibration is successful, the reference value is generated. If the calibration is recognized, there is no need to generate the reference value.

[0088] Step 109, save the calculated variance, and clear the attitude angles in each queue.

[0089] By saving the variance and clearing the queue, if the subsequent calibration fails, it can be considered that the attitude reference data is wirelessly obtained based on the existing images, and the attitude angle of the new image can be added to the queue, and the attitude reference data can be obtained again based on the attitude angle in the queue.

[0090] If the subsequent calibration is successful, when new attitude reference data needs to be obtained, the attitude angle of the new image can be added to the queue, so that the new attitude reference data can be obtained according to the attitude angle in the queue.

[0091] Clear the sample space by clearing the pose angles in the queue.

[0092] Step 110 , determining whether the calculated variances are not greater than the corresponding variance thresholds, if so, executing step 111 , otherwise executing step 101 .

[0093] It is feasible to determine whether the variance of the pitch angle is not greater than a preset pitch angle variance threshold, whether the variance of the yaw angle is not greater than a preset yaw angle variance threshold, and whether the variance of the roll angle is not greater than a preset roll angle variance threshold.

[0094] If the judgment result of step 110 is yes, the calibration can be considered successful, and the reference value obtained in step 107 can be used as the posture reference data. If the judgment result of step 110 is no, the calibration can be considered failed, and the reference value obtained in step 107 is not convenient to be used as the standard when the human head is in a normal position, then step 101 is re-executed to try to obtain the posture reference data again based on the new image. In one embodiment, this cycle can be repeated until the posture reference data is obtained.

[0095] Step 111, acquiring attitude reference data, where the attitude reference data includes a reference pitch angle, a reference yaw angle, and a reference roll angle.

[0096] The attitude reference data includes a reference pitch angle, a reference yaw angle, and a reference roll angle. The vector composed of these three attitude angles in 3D space can be regarded as a reference vector.

[0097] Figure 1 The illustrated embodiment can estimate the reference vector for the user's head and obtain the head posture reference vector. The head posture reference vector obtained based on the images captured by the camera within a period of time (e.g., within 5 minutes) can reflect the most normal position of the user's head during this period of time.

[0098] In one embodiment, multiple frames of images in a video can be collected separately, and the user head posture angle corresponding to each frame of the image can be obtained separately through a deep learning model. The continuous user head posture angles obtained over a period of time are used as the input of the head posture reference vector estimation algorithm, and combined with the kernel density estimation algorithm, the estimated head posture reference vector can be output.

[0099] The head posture reference vector can be used as a standard when the human head is in a normal position. This standard can be used to determine the movement state of the human head and then identify the human behavior. For example, in an application scenario in the field of assisted driving, the head posture reference vector can be combined to determine whether the driver is distracted while driving.

[0100] In this way, after obtaining the posture reference data, the movement state of the driver's head can be analyzed and processed according to the posture reference data, such as analyzing whether there is a large deviation of the driver's head compared to the posture reference data, so as to determine whether the driver is distracted during driving.

[0101] In one embodiment, taking the pitch angle as an example, the initialization parameter setting operation performed before the above step 101 may include the following:

[0102] Initialize queue input_pitch = [], size = 1500

[0103] Initialize calibration threshold T_var_pitch = 70

[0104] Set the scale x_array (-90~90), size=(181,1)

[0105] Set the probability distribution y_array, size = (181, 1)

[0106] Corresponding to the above steps 102 to 104, taking the pitch angle (pitch) as an example, after obtaining the pitch according to the current frame image (for example, recorded as the current frame pitch), the current frame pitch can be added to the queue input_pitch, and the kernel density function of the current frame pitch is calculated to realize the contribution of the current frame pitch on the entire scale (x_array), thereby completing the update of the probability density distribution of the pitch angle. The relevant implementation code program of this implementation process may include the following content:

[0107] input_pitch.append(pitch)#Join the team

[0108] for index,kick in enumerate(x_array):

[0109] y_array[index]+=gauss(kick-pitch) / bandwidth

[0110] Corresponding to the above steps 105 to 106, after the current frame pitch is added to the queue input_pitch, it can be determined whether the number of pitch angles in the queue reaches 1500. If the number of pitch angles in the queue input_pitch reaches 1500, the probability density function of the corresponding pitch angle can be calculated based on the kernel density function of the pitch angle in the queue input_pitch, and then the head posture reference vector can be calculated based on the probability density function of the corresponding pitch angle.

[0111] Corresponding to the above step 107, the angle corresponding to the maximum value of the probability density function can be used as the reference value, which is stable and has a unique solution. For example, the x_array corresponding to the maximum value of the y_array of pitch is used as the pitch angle reference angle (pitch_base), or the reference pitch angle. Similarly, the yaw angle reference angle (yaw_base), or the reference yaw angle, can be obtained, and the roll angle reference angle (roll_base), or the reference roll angle, can be obtained.

[0112] Corresponding to the above step 108, in order to calibrate the validity of the obtained reference value, the variance of each pitch angle in the queue input_pitch, the variance of each yaw angle in the queue input_yaw, and the variance of each roll angle in the queue input_roll can be solved.

[0113] Corresponding to the above steps 110 to 111, after solving the variance, the solved variance can be compared with the corresponding variance threshold. Among them, by comparing the variance of each pitch angle in the queue input_pitch with the set pitch angle variance threshold T_var_pitch, it is determined whether the calibration of the pitch angle is successful; by comparing the variance of each yaw angle in the queue input_yaw with the set yaw angle variance threshold T_var_yaw, it is determined whether the calibration of the yaw angle is successful; by comparing the variance of each roll angle in the queue input_roll with the set roll angle variance threshold T_var_roll, it is determined whether the calibration of the roll angle is successful. If all calibrations are successful, the estimated head posture reference vector (pitch_base, yaw_base, roll_base) can be obtained. The estimated head posture reference vector can be used to determine the movement state of the human head, and then identify the behavior of the human body.

[0114] In a test example, T_var_pitch=70, T_var_yaw=400, T_var_roll=150 are set, and by statistically analyzing the calibration results of the test samples, the comprehensive calibration success rate can be obtained to be 86%. The high comprehensive calibration success rate can indicate that the method for acquiring the attitude reference data provided by the embodiment of the present application can usually obtain accurate attitude reference data, and it is easy to obtain accurate attitude reference data.

[0115] Figure 4a A successful calibration example corresponding to the pitch angle is shown. Figure 4b A successful calibration example corresponding to the yaw angle is shown. Figure 5a A calibration failure example corresponding to the pitch angle is shown. Figure 5b An example of calibration failure corresponding to the yaw angle is shown.

[0116] Figure 4a In the figure, the horizontal axis is the pitch angle, and the vertical axis is the frequency. The variance calculated according to the pitch angle in the queue is Var=27.22, and the reference value of the pitch angle is base=3.49. Var=27.22<70, so the calibration of base=3.49 is successful.

[0117] Figure 4b In the figure, the horizontal axis is the yaw angle, and the vertical axis is the frequency. The variance calculated according to the yaw angle in the queue is Var=78.57, and the base value of the yaw angle is base=11.79. Var=78.57<400, so the calibration of base=11.79 is successful.

[0118] Figure 5a In the figure, the horizontal axis is the pitch angle, and the vertical axis is the frequency. The variance calculated according to the pitch angle in the queue is Var=111.77, and the reference value of the pitch angle is base=-0.35. Var=111.77>70, and the calibration of base=-0.35 fails.

[0119] Figure 5b In the figure, the horizontal axis is the yaw angle, and the vertical axis is the frequency. The variance calculated according to the yaw angle in the queue is Var=576.58, and the base value of the yaw angle is base=18.14. Var=576.58>400, and the calibration of base=18.14 fails.

[0120] Please refer to Figure 4a , Figure 4b , Figure 5a , Figure 5bThe calibration situation can be seen from the data distribution diagram shown, which shows that the variance can be used to filter out samples that do not meet the preset calibration requirements.

[0121] Figure 6a shows a reference vector estimation result corresponding to the pitch angle, Figure 6b shows a reference vector estimation result corresponding to the yaw angle, Figure 6c A reference vector estimation result corresponding to the roll angle is shown.

[0122] Figure 6a In the example, the variance calculated based on the pitch angle in the queue is Var=28.36, the base value of the pitch angle is base=15.00, Var=28.36<70, and the calibration of base=15.00 is successful.

[0123] Figure 6b In the example, the variance calculated based on the yaw angle in the queue is Var=181.97, the base value of the yaw angle is base=4.00, Var=181.97<400, and the calibration of base=4.00 is successful.

[0124] Figure 6c In the example, the variance calculated based on the roll angle in the queue is Var=129.02, the base value of the roll angle is base=15.27, Var=129.02<150, and the calibration of base=15.27 is successful.

[0125] Since the three reference values ​​are calibrated successfully, the estimated attitude reference vector can be (pitch_base=15.00, yaw_base=4.00, roll_base=15.27).

[0126] based on Figure 1 The embodiment shown can provide a method for estimating a user's head posture reference vector based on a video by using a kernel density estimation algorithm. Figure 1 The illustrated embodiment can have at least the following beneficial effects:

[0127] (1) Compared with the histogram method to fit the probability distribution to calculate the attitude reference vector, Figure 1 The illustrated embodiment calculates the attitude reference vector by using a kernel density estimation algorithm in a manner of fitting probability distribution, so that the attitude estimation can be more precise and accurate, and has stronger robustness.

[0128] (2) Figure 1The embodiment shown is simple, easy to implement and deploy. The whole process maintains and updates a probability density distribution variable in real time. Compared with other methods of estimating reference vectors using all values ​​at once, the instantaneous load of the CPU (central processing unit) is small, so that Figure 1 The illustrated embodiment may be deployed on an end device for computing.

[0129] (3) Figure 1 The illustrated embodiment uses sample variance to set a calibration threshold, and combined with the calibration threshold, samples that do not meet the requirements can be filtered out relatively well, thereby achieving the expected calibration purpose.

[0130] like Figure 7 As shown, an embodiment of the present application provides a method for acquiring attitude reference data, which may include steps 701 to 704:

[0131] Step 701: Acquire posture data of an object corresponding to the first image according to the first image.

[0132] Step 702: Acquire posture data of the object corresponding to the second image according to the second image.

[0133] In one embodiment, the first image and the second image may be images captured in real time by an image capture device, for example, they may be any frame image in a real-time video recording.

[0134] In another embodiment, the first image and the second image may also be non-real-time images captured by an image capture device, for example, they may be any frame image in an existing recorded video.

[0135] It is feasible that the first image is first acquired and step 701 is performed, and then the second image is acquired and step 702 is performed. In another embodiment, the second image is first acquired and step 702 is performed, and then the first image is acquired and step 701 is performed. This application does not limit this.

[0136] For any frame of image captured by the image acquisition device, the image may contain an image of the object or may not contain an image of the object (for example, when the object temporarily leaves the image acquisition area). In order to analyze the motion posture of the object, most images captured by the image acquisition device usually contain an image of the object.

[0137] The object can be the user's head, such as the driver's head, the test taker's head, etc., or it can be the user's whole body, the robot's whole body, a drone, etc.

[0138] In one embodiment, the attitude data may include at least one attitude angle of pitch angle, yaw angle and roll angle. For example, in one embodiment, the attitude data includes the pitch angle. In another embodiment, the attitude data includes the yaw angle and the roll angle. In another embodiment, the attitude data includes the pitch angle, yaw angle and roll angle.

[0139] In one embodiment, a deep learning model or other posture recognition algorithm can be used to obtain corresponding posture data of an object based on an image. For example, for an image captured by a camera for photographing a driver's head, a deep learning model can be used to obtain the pitch angle, yaw angle, and roll angle of the driver's head based on the image of the driver's head in the image.

[0140] Step 703: Obtain a first probability density function according to the posture data corresponding to the first image and the posture data corresponding to the second image.

[0141] The first image and the second image may be any two images among a plurality of images used to obtain the first probability density function.

[0142] In one embodiment, the number of images used to obtain the first probability density function may be multiple, for example, 1500. According to the posture data corresponding to these images, the probability density function of the posture data may be obtained.

[0143] In one embodiment, taking the attitude data including the pitch angle as an example, the probability density function corresponding to the pitch angle can be obtained according to the pitch angle in the attitude data. Similarly, the probability density function corresponding to the yaw angle and the probability density function corresponding to the roll angle can also be obtained.

[0144] Step 704: acquiring first posture reference data of the object according to the first probability density function, where the first posture reference data is posture data of the object when the object is in a first reference motion state.

[0145] The reference motion state of an object may include, for example, the body posture or head posture of a driver during normal driving, the normal sitting posture or head posture of a test taker during an exam, and the like.

[0146] Considering that the probability of an object maintaining a reference motion state for a period of time is usually high, the posture data with a high probability can be taken as the posture reference data of the object.

[0147] In one embodiment, the posture data corresponding to the maximum value of the probability density function may be taken as the posture reference data of the object.

[0148] In another embodiment, the optimal posture data among all the posture data used to obtain the first probability density function can be taken as the posture reference data of the object, and the value of the optimal posture data in the first probability density function is not less than the value of any other posture data in the first probability density function.

[0149] In one embodiment, the camera can collect 1500 frames of images of the driver's head within 5 minutes, and the posture data corresponding to the 1500 frames of images can be obtained based on the collected 1500 frames of images. A probability density function is then obtained based on the obtained posture data, and then the posture reference data of the driver's head is obtained based on the probability density function. The obtained posture reference data can reflect the driver's common driving posture within the 5 minutes, and the common driving posture can be defaulted to the head posture of the driver when driving the vehicle normally.

[0150] In one embodiment of the present application, the step of obtaining a first probability density function based on posture data corresponding to a first image and posture data corresponding to a second image may include: obtaining a first kernel density function based on the posture data corresponding to the first image; obtaining a second kernel density function based on the posture data corresponding to the second image; obtaining a first probability density function based on the first kernel density function and the second kernel density function.

[0151] It is feasible to use the posture data of an image as a sample point of the sample set, fit the posture data of each image to obtain the corresponding kernel density function, which is a probability density function, and then superimpose all the obtained kernel density functions to obtain the probability density function of the entire sample set.

[0152] In one embodiment, the kernel density function or kernel function may be a Gaussian function.

[0153] The number of posture data required to calculate the first probability density function is usually large, for example, 1500 posture data are required. In one embodiment, after the posture data of any image is acquired, the acquired posture data can be added to the posture data set, and a corresponding kernel density function can be generated based on the posture data of the image to achieve real-time maintenance and update of the probability density distribution of the posture data variable. Based on this, when the number of posture data reaches a preset threshold, the various kernel density functions that have been obtained can be directly superimposed to quickly obtain a probability density function. This implementation method reduces the instantaneous CPU load, so that the posture reference data acquisition method can be deployed on the terminal device for calculation.

[0154] In one embodiment, the posture reference data acquisition method may further include: when it is determined that the number of posture data in the first posture data set does not reach a preset quantity threshold, adding the posture data corresponding to the first image to the first posture data set to obtain a second posture data set; after obtaining the second posture data set, executing the step of obtaining a first kernel density function; after obtaining the first kernel density function, determining whether the number of posture data in the second posture data set reaches the quantity threshold; when it is determined that the number of posture data in the second posture data set does not reach the quantity threshold, adding the posture data corresponding to the second image to the second posture data set to obtain a third posture data set; after obtaining the third posture data set, executing the step of obtaining a second kernel density function; after obtaining the second kernel density function, determining whether the number of posture data in the third posture data set reaches the quantity threshold; when it is determined that the number of posture data in the third posture data set reaches the quantity threshold, executing the step of obtaining a first probability density function.

[0155] In one embodiment, the gesture data collection can be implemented in a queue. When a gesture data is obtained, the obtained gesture data can be added to the queue, and it is determined whether the number of gesture data in the queue reaches a quantity threshold. If the quantity threshold is not reached, the gesture data of the new image needs to be added, otherwise, the gesture data of the new image does not need to be added.

[0156] In one embodiment, the pitch angle in the attitude data may be added to a queue corresponding to the pitch angle, the yaw angle in the attitude data may be added to a queue corresponding to the yaw angle, and the roll angle in the attitude data may be added to a queue corresponding to the roll angle.

[0157] based on Figure 7 In the embodiment shown, assuming that the number of posture data in the queue has not reached 1500, the newly acquired posture data of the first image is added to the queue, and a corresponding kernel density function is generated based on the posture data of the first image. Since the posture data of the first image is newly added, it is determined again whether the number of posture data in the queue has reached 1500. Assuming that it has not reached 1500, the newly acquired posture data of the second image is added to the queue, and a corresponding kernel density function is generated based on the posture data of the second image. Since the posture data of the second image is newly added, it is determined again whether the number of posture data in the queue has reached 1500. Assuming that it has reached 1500, the generated kernel density functions corresponding to the various posture data in the queue can be superimposed to obtain a probability density function.

[0158] Different from the implementation method of real-time maintenance and updating of the probability density distribution of the posture data variable, in another embodiment, after determining that the number of posture data in the posture data set reaches a quantity threshold, a corresponding kernel density function can be generated based on each posture data in the posture data set, and then the generated kernel density functions can be superimposed to obtain a probability density function.

[0159] In one embodiment, in order to obtain accurate attitude reference data, the attitude reference data may be obtained first, and then the obtained attitude reference data may be calibrated (or verified) in combination with the variance. If the calibration is successful, the attitude reference data may be used to analyze the motion state of the object. If the calibration is performed for recognition, the attitude reference data may not be used to analyze the motion state of the object.

[0160] The variance of each posture data in the posture data set can be calculated and compared with a preset variance threshold, and the posture reference data can be calibrated according to the comparison result.

[0161] In another embodiment, in order to obtain accurate posture reference data, the variance can be combined to predict whether the posture reference data that can be obtained is accurate. Among them, the variance can be combined first to determine whether the posture reference data needs to be generated, so that the generated posture reference data is accurate data, otherwise it does not need to be generated.

[0162] In one embodiment of the present application, the posture data includes a first posture angle; the posture reference data acquisition method also includes: obtaining the variance of the first posture angle according to the first posture angle in the posture data corresponding to the first image and the first posture angle in the posture data corresponding to the second image; when the obtained variance meets the preset requirements, determining that the first posture reference data is available, or when the obtained variance meets the preset requirements, executing the step of obtaining a first probability density function; wherein the preset requirements include that the variance of the obtained first posture angle is not greater than a preset variance threshold of the first posture angle.

[0163] The first attitude angle may be any one of a pitch angle, a yaw angle, and a roll angle.

[0164] In one embodiment of the present application, the posture data includes a second posture angle; the posture reference data acquisition method also includes: obtaining the variance of the second posture angle based on the second posture angle in the posture data corresponding to the first image and the second posture angle in the posture data corresponding to the second image; wherein the preset requirement also includes that the second variance is not greater than a preset variance threshold of the second posture angle.

[0165] The posture data may have more than one posture angle. When the posture data includes more than one posture angle, the variance of each posture angle may be limited to be no greater than the corresponding variance threshold, and only then can it be considered that accurate posture reference data can be obtained based on the posture data in the posture data set. When the variance of any posture angle is greater than the corresponding variance threshold, it can be considered that accurate posture reference data cannot be obtained based on the posture data in the posture data set.

[0166] In one embodiment of the present application, the posture reference data acquisition method may further include: analyzing the motion state of the object according to the first posture reference data.

[0167] In one embodiment of the present application, the first image and the second image are both images captured by an image acquisition device; the step of analyzing the motion state of the object based on the first posture reference data may include: obtaining a target image captured by the image acquisition device in real time; obtaining posture data of the object corresponding to the target image based on the target image; obtaining an analysis result based on the first posture reference data and the posture data corresponding to the target image, and the analysis result is used to reflect whether the object is in a motion state corresponding to the first posture reference data.

[0168] After the attitude reference data is acquired, the attitude reference data can be applied to the object motion state analysis and processing in the following period of time.

[0169] After acquiring the attitude reference data, the image can be captured in real time. After a real-time image is captured, the attitude data of the real-time image is acquired, and the attitude data of the real-time image is compared with the attitude reference data. For example, the yaw angle in the attitude data of the real-time image is compared with the yaw angle in the attitude reference data to determine the difference between the two yaw angles. If the difference is large, it can be considered that the user's current motion state is greatly different from the motion state of the reference. If the difference is small, it can be considered that the user's current motion state is not much different from the motion state of the reference.

[0170] After the analysis and processing of the currently captured real-time image is completed, the next captured real-time image can be analyzed and processed, and the cycle continues. Thus, in one embodiment, after the step of obtaining the analysis result is completed, the step of obtaining the target image captured by the image acquisition device in real time can be performed again. In this way, real-time monitoring of the user's motion state can be achieved.

[0171] In one embodiment, the operation of acquiring the posture reference data may be performed only once, and the acquired posture reference data may be applied to the subsequent object motion state analysis process.

[0172] In another embodiment, the operation of acquiring posture reference data may be performed multiple times periodically or as needed, and the acquired posture reference data may be applied to the object motion state analysis process in a corresponding time period thereafter (in which no new posture reference data is acquired).

[0173] In one embodiment of the present application, the first image and the second image are both images captured by an image acquisition device within a first time period; the method for acquiring posture reference data may also include: acquiring posture data of the object corresponding to the third image based on the third image captured by the image acquisition device within the second time period; acquiring posture data of the object corresponding to the fourth image based on the fourth image captured by the image acquisition device within the second time period; obtaining a second probability density function based on the posture data corresponding to the third image and the posture data corresponding to the fourth image, wherein the second posture reference data is the posture data when the object is in a second reference motion state; acquiring the second posture reference data of the object based on the second probability density function; analyzing the motion state of the object within the second time period based on the first posture reference data; analyzing the motion state of the object within the third time period based on the second posture reference data; wherein the first time period, the second time period and the third time period are any three time periods that exist adjacent to each other in chronological order.

[0174] For example, the operation of acquiring the attitude reference data may be performed once every 5 minutes, and the attitude reference data obtained from the images collected within the 0th to 5th minute is applied to the object motion state analysis processing within the 6th to 10th minute. And the attitude reference data obtained from the images collected within the 6th to 10th minute is applied to the object motion state analysis processing within the 11th to 15th minute. And so on.

[0175] Since the posture reference data obtained each time is taken from the motion state of the object in the corresponding time period, it may not necessarily accurately reflect the reference motion state of the object. By periodically obtaining the corresponding posture reference data based on real-time posture data, and applying the obtained posture reference data to the analysis and processing of the object's motion state in the subsequent continuous time period, it helps to achieve accurate analysis and processing of the object's motion state.

[0176] like Figure 8 As shown, an embodiment of the present application provides a posture reference data acquisition device 80, including: a first acquisition module 801, a second acquisition module 802 and a third acquisition module 803.

[0177] Among them, the first acquisition module 801 is used to acquire the posture data of the object corresponding to the first image according to the first image; and acquire the posture data of the object corresponding to the second image according to the second image. The second acquisition module 802 is used to obtain the first probability density function according to the posture data corresponding to the first image and the posture data corresponding to the second image. The third acquisition module 803 is used to acquire the first posture reference data of the object according to the first probability density function, and the first posture reference data is the posture data when the object is in the first reference motion state.

[0178] An embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer executes the method described in any embodiment of the present application.

[0179] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program runs on a computer, the computer executes the method described in any embodiment of the present application.

[0180] Fig. 9 A schematic diagram of a computer device provided in an embodiment of the present application. Fig. 9 As shown, the computer device 20 of this embodiment includes: a processor 21 and a memory 22, the memory 22 is used to store a computer program 23 that can be run on the processor 21, and the computer program 23 is executed by the processor 21 to implement the steps in the method embodiment of the present application. To avoid repetition, they are not described one by one here. Alternatively, when the computer program 23 is executed by the processor 21, the functions of each model / unit in the device embodiment of the present application are implemented. To avoid repetition, they are not described one by one here.

[0181] The computer device 20 includes but is not limited to a processor 21 and a memory 22. Those skilled in the art will appreciate that Fig. 9 It is only an example of the computer device 20 and does not constitute a limitation of the computer device 20. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0182] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

[0183] The memory 22 may be an internal storage unit of the computer device 20, such as a hard disk or memory of the computer device 20. The memory 22 may also be an external storage device of the computer device 20, such as a plug-in hard disk, a Smart Media (SM) card, a Secure Digital (SD) card, a Flash Card, etc. equipped on the computer device 20. Further, the memory 22 may also include both an internal storage unit of the computer device 20 and an external storage device. The memory 22 is used to store the computer program 23 and other programs and data required by the computer device. The memory 22 may also be used to temporarily store data that has been output or is to be output.

[0184] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0185] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0186] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0187] The integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (Processor) to perform some steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program code.

[0188] In the embodiments of the present application, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, method, commodity or device including the element.

[0189] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0190] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0191] Those skilled in the art will appreciate that the various units and algorithm steps described in the embodiments of the present application can be implemented by a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0192] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0193] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for acquiring attitude reference data, characterized in that: include: According to the first image, acquiring posture data of the object corresponding to the first image; According to the second image, acquiring posture data of the object corresponding to the second image; Obtaining a first probability density function according to the posture data corresponding to the first image and the posture data corresponding to the second image; According to the first probability density function, first posture reference data of the object is acquired, where the first posture reference data is posture data when the object is in a first reference motion state.

2. The method according to claim 1, characterized in that The step of obtaining a first probability density function according to the posture data corresponding to the first image and the posture data corresponding to the second image comprises: Obtaining a first kernel density function according to the posture data corresponding to the first image; Obtaining a second kernel density function according to the posture data corresponding to the second image; A first probability density function is obtained according to the first kernel density function and the second kernel density function.

3. The method according to claim 2, characterized in that The method further includes: if it is determined that the amount of posture data in the first posture data set does not reach a preset amount threshold, adding the posture data corresponding to the first image to the first posture data set to obtain a second posture data set; After obtaining the second posture data set, performing the step of obtaining the first kernel density function; After obtaining the first kernel density function, determining whether the amount of posture data in the second posture data set reaches the amount threshold; When it is determined that the amount of posture data in the second posture data set does not reach the amount threshold, adding the posture data corresponding to the second image to the second posture data set to obtain a third posture data set; After obtaining the third posture data set, performing the step of obtaining the second kernel density function; After obtaining the second kernel density function, determining whether the amount of posture data in the third posture data set reaches the amount threshold; When it is determined that the number of posture data in the third posture data set reaches the number threshold, the step of obtaining the first probability density function is performed.

4. The method according to claim 1, characterized in that: The posture data includes a first posture angle; The method further includes: obtaining a variance of the first attitude angle according to the first attitude angle in the attitude data corresponding to the first image and the first attitude angle in the attitude data corresponding to the second image; When the obtained variance meets the preset requirement, determining that the first posture reference data is available, or when the obtained variance meets the preset requirement, executing the step of obtaining the first probability density function; The preset requirement includes that the variance of the obtained first attitude angle is not greater than a preset variance threshold of the first attitude angle.

5. The method according to claim 1, characterized in that: The method further includes: analyzing the motion state of the object according to the first posture reference data.

6. The method according to claim 5, characterized in that The first image and the second image are both images captured by an image capture device; The analyzing the motion state of the object according to the first posture reference data includes: Acquire the target image acquired by the image acquisition device in real time; According to the target image, acquiring posture data of the object corresponding to the target image; An analysis result is obtained according to the first posture reference data and the posture data corresponding to the target image, and the analysis result is used to reflect whether the object is in a motion state corresponding to the first posture reference data.

7. The method according to claim 1, characterized in that The first image and the second image are both images captured by an image capture device within a first time period; The method further includes: acquiring posture data of the object corresponding to the third image according to the third image acquired by the image acquisition device within the second time period; Acquire, according to a fourth image acquired by the image acquisition device within the second time period, posture data of the object corresponding to the fourth image; Obtaining a second probability density function according to the posture data corresponding to the third image and the posture data corresponding to the fourth image; According to the second probability density function, obtaining second posture reference data of the object, where the second posture reference data is posture data when the object is in a second reference motion state; analyzing the motion state of the object in the second time period according to the first posture reference data; analyzing the motion state of the object in a third time period according to the second posture reference data; The first time period, the second time period and the third time period are any three time periods that are adjacent to each other in chronological order.

8. A posture reference data acquisition device, characterized in that: include: A first acquisition module, configured to acquire, based on a first image, posture data of an object corresponding to the first image; According to the second image, acquiring posture data of the object corresponding to the second image; A second acquisition module, configured to obtain a first probability density function according to the posture data corresponding to the first image and the posture data corresponding to the second image; The third acquisition module is used to acquire first posture reference data of the object according to the first probability density function, where the first posture reference data is posture data of the object when it is in a first reference motion state.

9. An electronic device, characterized in that: The electronic device comprises a memory for storing computer program instructions, a processor for executing the computer program instructions, and a communication device, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to execute the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed on a computer, enables the computer to execute the method according to any one of claims 1 to 7.

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