CN113706688A - Dynamic human body size characteristic modeling method - Google Patents
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Abstract
The invention discloses a dynamic human body dimension characteristic modeling method, wherein the dynamic human body dimension characteristic modeling scheme comprises the following steps: step 1: obtaining basic characteristics of a user through a human body three-dimensional scanner; step 2: and model calculation, namely calculating net size data by using the basic characteristics. After the early-stage weight and height data are measured, a body type model of a client can be roughly inferred through a calculation formula, basic characteristics, group characteristics and individual characteristics of the user are input into modeling, net size data are calculated by utilizing the basic characteristics, data such as chest circumference, abdomen circumference, shoulder width and the like are calculated, group characteristics of the user are input, wherein the data comprise chest shape, abdomen shape, shoulder shape and back shape, group characteristic size data are obtained through model calculation, the individual characteristics are added into the model for calculation, and individual characteristic data are obtained, so that 99% of conventional size requirements and 1% of special size requirements are met.
Description
Technical Field
The invention relates to the technical field of human body individual characteristic dimension correction, in particular to a dynamic human body dimension characteristic modeling method.
Background
With the popularization of the internet, online shopping plays an increasingly important role, and in daily life, measurement of a human body is a common requirement, for example, when a person purchases clothes, measurement of each part of the human body is needed to obtain the size of each part of the human body. The human body feature recognition function of the terminal equipment can be matched with a pre-stored human body feature template by collecting the human body features of the user, so that certain functions of the terminal, such as terminal screen unlocking, application decryption, mobile payment and the like, are realized according to the matching result.
At present, in the existing human body characteristic modeling, a three-dimensional acquisition device is adopted to acquire multiple data of a human body, a special human body characteristic template acquisition and a human body characteristic password setting flow need to be executed, the execution process is complex, a certain time of a user can be consumed, and the user experience is influenced. Therefore, a new technical solution needs to be provided.
Disclosure of Invention
The invention aims to provide a dynamic human body dimension characteristic modeling method, which solves the problems that the existing human body characteristic modeling adopts three-dimensional acquisition equipment to acquire multiple data of a human body, a special human body characteristic template acquisition and a human body characteristic password setting process need to be executed, the execution process is complicated, a certain time of a user is consumed, and the user experience is influenced.
In order to achieve the purpose, the invention provides the following technical scheme: a dynamic human dimension feature modeling method, the dynamic human dimension feature modeling scheme comprising the steps of:
step 1: obtaining basic characteristics of a user through a human body three-dimensional scanner;
step 2: model calculation, namely calculating net size data by using basic characteristics;
and step 3: more accurate depth information is obtained through a mobile phone with a LIDAR, so that a plurality of group characteristics of the body of a user are obtained;
and 4, step 4: according to the net size data and the group characteristics, performing model calculation again to obtain characteristic size data;
and 5: inputting individual characteristics of a user;
step 6: correcting the characteristic size data of the user, recording the individual characteristic data of the user and adding the individual characteristic data to the general characteristic size data obtained in the step 4;
and 7: with the above obtained net size, feature size, and corrected feature size data, a size library is created to meet 99% of the conventional size requirements and 1% of the special size requirements.
As a preferred embodiment of the present invention, the basic characteristics of the user obtained in step 1 include weight and height.
As a preferred embodiment of the present invention, the model calculation method of step 2: assuming that the net chest circumference is related to the height and weight of the body as follows, y is a x1+ b x2+ c; wherein, y chest circumference data is dependent variable, x1 height and x2 weight are independent variables, a and b are regression coefficients, and c is regression constant; through n groups of corresponding data, the coefficients and constants of the regression equation can be solved; other net size data such as abdominal circumference, shoulder width, etc. can also be solved by regression equations.
As a preferred embodiment of the present invention, the population characteristics of step 3 include: chest, abdomen, hip, shoulder and back, user features are general size-influencing features which are summarized by a large amount of past data.
As a preferred embodiment of the present invention, the model calculation method in step 4 is: firstly, the population characteristics of the step 3 are represented digitally, such as chest type flatness is represented by 1, and chest type muscle is represented by 2; the final feature size is determined by both the net size data and the population features, where we use a multiple linear regression model to calculate.
As a preferred embodiment of the present invention, the individual characteristics in step 5 include: the upper garment and the lower garment are in sizes, wherein the upper garment comprises a front garment length, a shoulder width, a collar circumference, a chest circumference, an abdomen circumference, a hip circumference, a sleeve length, a sleeve fat and a wrist circumference, and the lower garment comprises a trousers skirt length, a waist circumference, a hip circumference, a knee circumference, a foot opening, a crosspiece and a general wave.
Compared with the prior art, the invention has the following beneficial effects:
after the early-stage weight and height data are measured, a body type model of a client can be roughly inferred through a calculation formula, basic characteristics, group characteristics and individual characteristics of the user are input into modeling, net size data are calculated by utilizing the basic characteristics, data such as chest circumference, abdomen circumference, shoulder width and the like are calculated, group characteristics of the user are input, wherein the data comprise chest shape, abdomen shape, shoulder shape and back shape, group characteristic size data are obtained through model calculation, the individual characteristics are added into the model for calculation to obtain individual characteristic data, and therefore the requirements of 99% of conventional size and 1% of special size are met.
Drawings
FIG. 1 is a schematic overall flow diagram of the present invention;
FIG. 2 is a flow chart illustrating individual features of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: a dynamic human dimension feature modeling method, the dynamic human dimension feature modeling scheme comprising the steps of:
step 1: obtaining basic characteristics of a user through a human body three-dimensional scanner;
step 2: model calculation, namely calculating net size data by using basic characteristics;
and step 3: more accurate depth information is obtained through a mobile phone with a LIDAR, so that a plurality of group characteristics of the body of a user are obtained;
and 4, step 4: according to the net size data and the group characteristics, performing model calculation again to obtain characteristic size data;
and 5: inputting individual characteristics of a user;
step 6: correcting the characteristic size data of the user, recording the individual characteristic data of the user and adding the individual characteristic data to the general characteristic size data obtained in the step 4;
and 7: with the above obtained net size, feature size, and corrected feature size data, a size library is created to meet 99% of the conventional size requirements and 1% of the special size requirements.
Further improved, as shown in fig. 1: the basic characteristics of the user obtained in the step 1 comprise weight and height.
Further improved, as shown in fig. 1: the model calculation mode of the step 2 is as follows: assuming that the net chest circumference is related to the height and weight of the body as follows, y is a x1+ b x2+ c; wherein, y chest circumference data is dependent variable, x1 height and x2 weight are independent variables, a and b are regression coefficients, and c is regression constant; through n groups of corresponding data, the coefficients and constants of the regression equation can be solved; other net size data such as abdominal circumference, shoulder width, etc. can also be solved by regression equations.
Further improved, as shown in fig. 1: the population characteristics of the step 3 comprise: chest, abdomen, hip, shoulder and back, user features are general size-influencing features which are summarized by a large amount of past data.
Further improved, as shown in fig. 1: the model calculation mode of the step 4 is as follows: firstly, the population characteristics of the step 3 are represented digitally, such as chest type flatness is represented by 1, and chest type muscle is represented by 2; the final feature size is determined by the net size data and the group features, and a multivariate linear regression model is adopted for calculation; firstly, the equation needs to be subjected to significance type test and divided into two parts, wherein the first part is t test, each independent variable is subjected to significance test, namely whether each independent variable has significance influence on y is judged, the second part is goodness of fit, namely R2, the value of the goodness of fit is between 0 and 1, the closer to 1, the better the regression fitting effect is, the closer to 0, the worse the effect is; after the significance type test is finished, independent variables which are not significant in linear relation with y are removed, and independent variables which are significant in relation are left, such as the chest circumference of a user is significantly related to the height, the weight and the chest shape, and the back shape is not significantly related, so that the significant correlation is solved to obtain an effective multiple linear regression model.
Further improved, as shown in fig. 1: the individual characteristics in the step 5 comprise: the length and dimension of the upper arm, the length and dimension of the lower arm, the length and dimension of the thigh and the length and dimension of the shank.
After the early-stage weight and height data are measured, a body type model of a client can be roughly inferred through a calculation formula, basic characteristics, group characteristics and individual characteristics of the user are input into modeling, net size data are calculated by utilizing the basic characteristics, data such as chest circumference, abdomen circumference, shoulder width and the like are calculated, group characteristics of the user are input, wherein the data comprise chest shape, abdomen shape, shoulder shape and back shape, group characteristic size data are obtained through model calculation, the individual characteristics are added into the model for calculation, and individual characteristic data are obtained, so that 99% of conventional size requirements and 1% of special size requirements are met.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A dynamic human body dimension feature modeling method is characterized in that: the dynamic human body dimension feature modeling scheme comprises the following steps:
step 1: obtaining basic characteristics of a user through a human body three-dimensional scanner;
step 2: model calculation, namely calculating net size data by using basic characteristics;
and step 3: more accurate depth information is obtained through a mobile phone with a LIDAR, so that a plurality of group characteristics of the body of a user are obtained;
and 4, step 4: according to the net size data and the group characteristics, performing model calculation again to obtain characteristic size data;
and 5: inputting individual characteristics of a user;
step 6: correcting the characteristic size data of the user, recording the individual characteristic data of the user and adding the individual characteristic data to the general characteristic size data obtained in the step 4;
and 7: with the above obtained net size, feature size, and corrected feature size data, a size library is created to meet 99% of the conventional size requirements and 1% of the special size requirements.
2. The dynamic human body dimension feature modeling method of claim 1, wherein: the basic characteristics of the user obtained in the step 1 comprise weight and height.
3. The dynamic human body dimension feature modeling method of claim 1, wherein: the model calculation mode of the step 2 is as follows: assuming that the net chest circumference is related to the height and weight of the body as follows, y is a x1+ b x2+ c; wherein, y chest circumference data is dependent variable, x1 height and x2 weight are independent variables, a and b are regression coefficients, and c is regression constant; through n groups of corresponding data, the coefficients and constants of the regression equation can be solved; other net size data such as abdominal circumference, shoulder width, etc. can also be solved by regression equations.
4. The dynamic human body dimension feature modeling method of claim 1, wherein: the population characteristics of the step 3 comprise: chest, abdomen, hip, shoulder and back, user features are general size-influencing features which are summarized by a large amount of past data.
5. The dynamic human body dimension feature modeling method of claim 1, wherein: the model calculation mode of the step 4 is as follows: firstly, the population characteristics of the step 3 are represented digitally, such as chest type flatness is represented by 1, and chest type muscle is represented by 2; the final feature size is determined by both the net size data and the population features, where we use a multiple linear regression model to calculate.
6. The dynamic human body dimension feature modeling method of claim 1, wherein: the individual characteristics in the step 5 comprise: the upper garment and the lower garment are in sizes, wherein the upper garment comprises a front garment length, a shoulder width, a collar circumference, a chest circumference, an abdomen circumference, a hip circumference, a sleeve length, a sleeve fat and a wrist circumference, and the lower garment comprises a trousers skirt length, a waist circumference, a hip circumference, a knee circumference, a foot opening, a crosspiece and a general wave.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20020029150A (en) * | 2000-10-12 | 2002-04-18 | 전홍건 | clothes electronic commerce business system using body sizes system and measuring method therefor |
| CN110009730A (en) * | 2019-03-26 | 2019-07-12 | 深圳大学 | A kind of modeling method based on smart machine three-dimensional human body measurement |
| CN111047407A (en) * | 2019-12-13 | 2020-04-21 | 南京中略信息技术有限公司 | Clothing personalized size customization method using variational multidimensional regression |
| CN112419479A (en) * | 2020-11-10 | 2021-02-26 | 广州二元科技有限公司 | Body type data calculation method based on weight, height and body image |
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- 2021-07-22 CN CN202110831856.3A patent/CN113706688A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20020029150A (en) * | 2000-10-12 | 2002-04-18 | 전홍건 | clothes electronic commerce business system using body sizes system and measuring method therefor |
| CN110009730A (en) * | 2019-03-26 | 2019-07-12 | 深圳大学 | A kind of modeling method based on smart machine three-dimensional human body measurement |
| CN111047407A (en) * | 2019-12-13 | 2020-04-21 | 南京中略信息技术有限公司 | Clothing personalized size customization method using variational multidimensional regression |
| CN112419479A (en) * | 2020-11-10 | 2021-02-26 | 广州二元科技有限公司 | Body type data calculation method based on weight, height and body image |
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