CN115284976B - Automatic adjustment method, device and equipment for vehicle seat and storage medium - Google Patents
Automatic adjustment method, device and equipment for vehicle seat and storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
- B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
- B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
- B60N2/0224—Non-manual adjustments, e.g. with electrical operation
- B60N2/0244—Non-manual adjustments, e.g. with electrical operation with logic circuits
- B60N2/0248—Non-manual adjustments, e.g. with electrical operation with logic circuits with memory of positions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
- B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
- B60N2/002—Seats provided with an occupancy detection means mounted therein or thereon
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
- B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
- B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
- B60N2/0224—Non-manual adjustments, e.g. with electrical operation
- B60N2/0244—Non-manual adjustments, e.g. with electrical operation with logic circuits
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
- B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
- B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
- B60N2/0224—Non-manual adjustments, e.g. with electrical operation
- B60N2/0244—Non-manual adjustments, e.g. with electrical operation with logic circuits
- B60N2/0268—Non-manual adjustments, e.g. with electrical operation with logic circuits using sensors or detectors for adapting the seat or seat part, e.g. to the position of an occupant
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
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- G—PHYSICS
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
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- G—PHYSICS
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- G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
- G06V20/593—Recognising seat occupancy
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- G—PHYSICS
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
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Abstract
The invention discloses a vehicle seat automatic adjusting method, a device, equipment and a storage medium, wherein the method comprises the following steps: when passengers are detected in the carriage, acquiring images of the passengers through the vehicle-mounted camera; face recognition is carried out on the face image in the image, and whether the face data of the passengers exist in a preset database is determined according to the face recognition result; when the face data of the passengers exist in the preset database, acquiring seat data corresponding to the face data from the preset database; and automatically adjusting the vehicle seat corresponding to the passenger according to the seat data. According to the invention, when the passenger is detected in the carriage, the face image in the passenger image is recognized, and when the face data of the passenger exists in the preset database according to the result of the face recognition, the seat data corresponding to the face data is acquired, and the vehicle seat is automatically regulated according to the seat data, so that the regulation accuracy of the seat is improved, and the regulation efficiency is improved.
Description
Technical Field
The present invention relates to the field of automotive technologies, and in particular, to a method, an apparatus, a device, and a storage medium for automatically adjusting a vehicle seat.
Background
With the development of technology, automobiles are increasingly popular, and the requirements of people on the comfort of driving and riding the automobiles are higher, so that the intelligent degree of the automobiles is required to be improved to meet the requirements of clients, the automobile seats are often adjusted to be most suitable for the positions of the drivers or passengers when the drivers or passengers drive or ride the automobiles, the automobile seats are generally required to be readjusted when different drivers or passengers are replaced, the seats are generally adjusted in a manual mode at present, and the adjustment accuracy is low and the time is long.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide an automatic vehicle seat adjusting method, device, equipment and storage medium, and aims to solve the technical problems of low accuracy and long time consumption in adjusting a vehicle seat in the prior art.
To achieve the above object, the present invention provides a vehicle seat automatic adjustment method comprising the steps of:
when a passenger is detected in a carriage, acquiring an image of the passenger through a vehicle-mounted camera;
carrying out face recognition on the face image in the image, and determining whether the face data of the passengers exist in a preset database according to the result of the face recognition;
when the face data of the passengers exist in the preset database, acquiring seat data corresponding to the face data from the preset database;
and automatically adjusting the vehicle seat corresponding to the passenger according to the seat data.
Optionally, the performing face recognition on the face image in the image, and determining whether the face data of the passenger exists in a preset database according to the result of the face recognition, includes:
classifying the images through a preset classification mode, and determining whether a face image exists in the images according to classification results;
when a face image exists in the image, carrying out face recognition on the face image through a first preset convolutional neural network model;
determining a face characteristic value of the face image according to a face recognition result;
and determining whether the face data of the passengers exist in a preset database according to the face characteristic values.
Optionally, the determining whether the face data of the passenger exists in the preset database according to the face feature value includes:
determining the face phase difference degree corresponding to each stored face data according to the face characteristic value and the stored sign values of a plurality of stored face data in a preset database;
and if the face phase difference degree is larger than or equal to a preset phase difference degree threshold value, judging that the face data of the passengers exist in the preset database.
Optionally, the determining the face phase difference degree corresponding to each stored face data according to the face feature value and the stored sign values of the plurality of stored face data in the preset database includes:
according to the face characteristic values and the stored characteristic values of a plurality of stored face data in a preset database, determining the face phase difference corresponding to each stored face data through a preset phase difference formula;
the preset phase difference formula is as follows:
wherein d (x, y (j)) is the phase difference; a is the number of face feature values; n is the number of stored face data; x is x i The i-th face feature value; y (j) i The ith face feature value of the face data is stored for the jth.
Optionally, after the face recognition is performed on the face image in the image and whether the face data of the passenger exists in the preset database is determined according to the result of the face recognition, the method further includes:
acquiring physical characteristic data of the passengers when the facial data of the passengers do not exist in the preset database;
inputting the physical characteristic data into a second preset convolutional neural network model, and acquiring fitting seat data output by the second preset convolutional neural network model;
and automatically adjusting the vehicle seat corresponding to the passenger according to the fitting seat data.
Optionally, when the face data of the passenger does not exist in the preset database, acquiring physical characteristic data of the passenger includes:
when the face data of the passengers do not exist in the preset database, acquiring pressure data, which are acquired through a seat sensor, of the corresponding vehicle seats of the passengers, wherein the pressure data comprise headrest pressure, backrest pressure and cushion pressure;
according to the pressure data, determining physical characteristic data of the passengers through a preset conversion formula;
the preset conversion formula is as follows:
R＝α*H+β*(B+S)
wherein R is a body characteristic value; alpha is a first weight coefficient; h is the headrest pressure; beta is a second weight coefficient; b is the back pressure; s is cushion pressure.
Optionally, after the vehicle seat corresponding to the occupant is automatically adjusted according to the fitting seat data, the method further includes:
and when the adjustment of the vehicle seat is completed, storing the facial data and the fitting seat data into the preset database correspondingly.
In addition, in order to achieve the above object, the present invention also proposes an automatic adjustment device for a vehicle seat, the device comprising:
the image acquisition module is used for acquiring images of passengers through the vehicle-mounted camera when the passengers are detected in the carriage;
the determining module is used for carrying out face recognition on the face image in the image and determining whether the face data of the passengers exist in a preset database according to the result of the face recognition;
the data acquisition module is used for acquiring seat data corresponding to the face data from the preset database when the face data of the passengers exist in the preset database;
and the adjusting module is used for automatically adjusting the vehicle seat corresponding to the passenger according to the seat data.
In addition, in order to achieve the above object, the present invention also proposes an automatic adjustment device for a vehicle seat, the device comprising: a memory, a processor, and a vehicle seat auto-adjustment program stored on the memory and executable on the processor, the vehicle seat auto-adjustment program configured to implement the steps of the vehicle seat auto-adjustment method as described above.
In addition, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon a vehicle seat automatic adjustment program which, when executed by a processor, implements the steps of the vehicle seat automatic adjustment method as described above.
When an occupant is detected in a carriage, an image of the occupant is acquired through a vehicle-mounted camera; carrying out face recognition on the face image in the image, and determining whether the face data of the passengers exist in a preset database according to the result of the face recognition; when the face data of the passengers exist in the preset database, acquiring seat data corresponding to the face data from the preset database; and automatically adjusting the vehicle seat corresponding to the passenger according to the seat data. Because the invention carries out face recognition on the face image in the image of the passenger when the passenger is detected in the carriage, obtains the seat data corresponding to the face data when the face data of the passenger exists in the preset database according to the face recognition result, and automatically adjusts the vehicle seat according to the seat data, the technical problem that the vehicle seat needs to be manually adjusted in the prior art is solved, and the adjustment accuracy of the seat is improved and the adjustment efficiency is improved.
Drawings
FIG. 1 is a schematic view of a vehicle seat automatic adjustment apparatus in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flow chart of a first embodiment of the automatic adjustment method of a vehicle seat according to the present invention;
FIG. 3 is a schematic diagram showing the connection relationship between a control unit and various devices and units in an embodiment of the automatic adjustment method of a vehicle seat according to the present invention;
FIG. 4 is a schematic diagram illustrating a classification process of SVM models according to an embodiment of the automatic adjustment method of a vehicle seat of the present invention;
FIG. 5 is a schematic diagram of a ResNet18 network architecture in an embodiment of a vehicle seat auto-adjustment method according to the present invention;
FIG. 6 is a schematic view of a residual block in an embodiment of an automatic adjustment method of a vehicle seat according to the present invention;
FIG. 7 is a flow chart of a second embodiment of the automatic adjustment method of a vehicle seat according to the present invention;
FIG. 8 is a flow chart of a third embodiment of the automatic adjustment method of a vehicle seat according to the present invention;
fig. 9 is a block diagram showing the construction of a first embodiment of the automatic vehicle seat adjusting apparatus of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an automatic vehicle seat adjusting apparatus in a hardware running environment according to an embodiment of the present invention.
As shown in fig. 1, the vehicle seat automatic adjustment apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 does not constitute a limitation of the vehicle seat automatic adjustment apparatus, and may include more or fewer components than shown, or may combine certain components, or may have a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a vehicle seat automatic adjustment program may be included in the memory 1005 as one type of storage medium.
In the vehicle seat automatic adjustment apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the vehicle seat automatic adjustment apparatus of the present invention may be provided in the vehicle seat automatic adjustment apparatus, which invokes the vehicle seat automatic adjustment program stored in the memory 1005 through the processor 1001 and executes the vehicle seat automatic adjustment method provided by the embodiment of the present invention.
An embodiment of the invention provides an automatic adjustment method for a vehicle seat, referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the automatic adjustment method for a vehicle seat.
In this embodiment, the automatic adjustment method for a vehicle seat includes the steps of:
step S1: when an occupant is detected in the carriage, an image of the occupant is acquired by the vehicle-mounted camera.
It should be noted that, the execution body of the embodiment may be a computing service device having functions of data processing, network communication and program running, for example, a driving computer, a vehicle-mounted computer, a central control unit of an automobile, or an electronic device, an automatic adjustment device of a vehicle seat, or the like capable of realizing the above functions. Hereinafter, this embodiment and the following embodiments will be described by way of example using a central control unit (simply referred to as a control unit) of an automobile.
It can be understood that the passenger includes a driver and a passenger, and the vehicle seat automatic adjustment method provided by the embodiment can be used for adjusting the vehicle seat at the driving position and also can be used for adjusting the vehicle seat at the passenger position; whether passengers exist in the carriage or not can be detected through a pressure sensor and/or a vehicle-mounted camera; the image of the occupant may be an image taken by an onboard camera that contains the occupant's body and the environment in which the occupant is located.
In specific implementation, the external pressure received by the vehicle seat is collected through the pressure sensor, whether a person enters the carriage is detected through the vehicle-mounted camera, and when the external pressure collected through the pressure sensor is greater than preset pressure and/or the vehicle-mounted camera detects that the person enters the carriage, the image of the person is shot through the vehicle-mounted camera.
Step S2: and carrying out face recognition on the face image in the image, and determining whether the face data of the passengers exist in a preset database according to the face recognition result.
It will be appreciated that the face image may be an image containing only the occupant's face; the preset database may be a preset database storing correspondence between face data and seat data; the face data may be face feature data of the occupant.
Step S3: and when the face data of the passengers exist in the preset database, acquiring seat data corresponding to the face data from the preset database.
It can be understood that when the face data of the occupant exists in the preset database, it is determined that the seat data matched with the occupant exists in the preset database; the seat data includes cushion data, backrest data, and headrest data, the cushion data may be front-rear position data of a seat cushion of the vehicle, the backrest data may be inclination data of a backrest of the vehicle, and the headrest data may be up-down position data of a headrest of the vehicle.
Step S4: and automatically adjusting the vehicle seat corresponding to the passenger according to the seat data.
In a specific implementation, referring to fig. 3, fig. 3 is a schematic diagram of a connection relationship between a control unit and each device and unit, the control unit collects external pressure received by a vehicle seat through a pressure sensor arranged on a cushion of the vehicle seat, detects whether a person enters a carriage through a vehicle-mounted camera, when the external pressure collected by the pressure sensor is greater than a preset pressure value and/or the person enters the carriage through the vehicle-mounted camera, images of passengers entering the carriage are shot through the vehicle-mounted camera, a plurality of images can be continuously shot according to preset frequency, an image with highest definition is selected from the images, the images are preprocessed, face images in the preprocessed images are reserved, face recognition is performed on the face images, whether face data of the passengers exist in a preset database is determined according to face recognition results, if so, cushion data, backrest data and headrest data corresponding to the face data are acquired, the control unit controls the cushion adjusting unit to adjust the front and back positions of the cushion of the vehicle seat according to the cushion data, the backrest inclination angle of the backrest adjusting unit controls the headrest adjusting unit to adjust the up and down positions of the vehicle seat according to the headrest data, the preprocessing comprises adjusting contrast, denoising, preprocessing of the contrast, preprocessing of the images, and the headrest matrix image format of the images can be further be stored.
Further, in order to improve the accuracy of face recognition, the step S2 includes:
step S21: classifying the images through a preset classification mode, and determining whether the images have face images according to classification results.
It can be appreciated that the preset classification mode may be a preset trained SVM model, and classifying whether a face exists in the image through the preset trained SVM model; in the training process of the SVM model, a large number of pictures with human faces originally existing and pictures without human faces originally existing are utilized to train an initial SVM model to obtain a preset classification model, the preset classification model is stored in a central control unit, after images of passengers are obtained through a vehicle-mounted camera, the pictures are input into the preset SVM model to be classified, and whether the human faces exist in the pictures is judged.
In this embodiment, the SVM model is adopted as the classification model, because there are few data samples that can be obtained in the vehicle-mounted system, and the SVM model can effectively solve the classification problem under the condition of small samples, meanwhile, the vehicle-mounted system has high requirements for classification speed, and the SVM model is adopted as the classification model, so that the input image can be classified in two times, and whether the face image exists in the image can be rapidly judged, thereby improving the classification efficiency.
It will be appreciated that referring to fig. 4, fig. 4 is a schematic diagram of a classification process of an SVM model, in which the SVM model determines a classification line B line by finding an optimal decision boundary (refer to two dotted lines in fig. 2), in this embodiment, points on the upper right and lower left of the B line represent a picture containing a face and a picture not containing a face, respectively, and after the SVM model is pre-trained, the B line is obtained, where the B line meets the equation W T * X+b=0, and is a positive sample when the face is contained in the picture, which accords with equation W T * X+b is more than or equal to 0; otherwise, the negative sample is adopted, which accords with the equation W T * X+b is less than or equal to 0, wherein W and b are coefficients of classification lines, the coefficients are determined by a pre-trained SVM model, X is the attribute of a sample, and generally the dimension of X is the same as the number of the samples.
Step S22: and when the face image exists in the image, carrying out face recognition on the face image through a first preset convolutional neural network model.
It may be appreciated that the first preset convolutional neural network may be a preset trained convolutional neural network, and the first preset convolutional neural network model in this embodiment may be a trained res net18 convolutional neural network model.
In this embodiment, the VGGFace2 face data set is used to pretrain the network, so that the network can select the face of a passenger in a picture shot by a camera, and the res net is used as a classical classification CNN model, so that the problem that the model is easy to fit excessively when the number of layers of the CNN network is deepened is solved, and the res net18 adopted in the embodiment has a simple architecture, is easy to train and light in weight, and can well run on a vehicle-mounted system no matter whether text data classification or picture data classification is processed.
It will be appreciated that with reference to fig. 5 and 6, fig. 5 is a schematic diagram of a res net18 network architecture; fig. 6 is a schematic structural diagram of a residual block; resNet18 has consulted the idea of VGG block, has constructed the concept of the residual block, the main network is connected together by a series of residual blocks to form, the overall structure is by 1 convolution layer as input, 8 residual blocks are used for extracting the characteristic, 1 average pooling layer and 1 all link layer as output; the ResNet18 convolutional neural network mainly uses ResNet network blocks to carry out iterative training on a face data set for a plurality of times in advance, so that the network can identify the face in the picture.
Step S23: and determining the face characteristic value of the face image according to the face recognition result.
It will be appreciated that the face feature values can be feature values extracted from the face image by the pre-trained ResNet 18.
Step S24: and determining whether the face data of the passengers exist in a preset database according to the face characteristic values.
In a specific implementation, classifying an image of an occupant through a pre-trained SVM model, determining whether the image contains a face image or not according to a classification result, if so, extracting feature values from the face image through a pre-trained ResNet18 network, comparing the extracted feature values with feature values in a preset database, and determining whether face data of the occupant exists in the preset database according to a comparison result, wherein the extracted feature values can be feature values of face feature points on the face image, the face feature points comprise eyebrow feature points, eye feature points, nose feature points and mouth feature points, 68 feature points are input into the ResNet18 network, and 128 feature values are generated, namely 128 feature values corresponding to each face image.
When an occupant is detected in a carriage, the image of the occupant is acquired through a vehicle-mounted camera; carrying out face recognition on the face image in the image, and determining whether the face data of the passengers exist in a preset database according to the result of the face recognition; when the face data of the passengers exist in the preset database, acquiring seat data corresponding to the face data from the preset database; and automatically adjusting the vehicle seat corresponding to the passenger according to the seat data. Because the face image in the passenger image is recognized when the passenger is detected in the carriage, the seat data corresponding to the face data is acquired when the face data of the passenger exists in the preset database according to the recognition result, and the vehicle seat is automatically adjusted according to the seat data, the technical problem that the vehicle seat is required to be manually adjusted in the prior art is solved, and the adjustment efficiency is improved while the adjustment accuracy of the seat is improved.
Referring to fig. 7, fig. 7 is a flowchart illustrating a second embodiment of the automatic adjustment method for a vehicle seat according to the present invention.
Based on the first embodiment, in this embodiment, the step S24 includes:
step S241: and determining the face phase difference degree corresponding to each stored face data according to the face characteristic value and the stored sign values of a plurality of stored face data in a preset database.
It can be understood that the stored feature values may be face feature values corresponding to each face data stored in the preset data, and the number of the stored feature values is consistent with the number of the face feature values; the face difference degree may be a degree of matching of the face data of the occupant with the face data stored in the preset database.
Step S242: and if the face phase difference degree is larger than or equal to a preset phase difference degree threshold value, judging that the face data of the passengers exist in the preset database.
It may be understood that the preset phase difference threshold may be a preset value, and if the phase difference of the face is greater than or equal to the preset phase difference threshold, it is determined that the corresponding stored face data and the face data of the occupant are face data of the same person, that is, the face data of the occupant exists in the preset database.
Further, in order to accurately determine whether the face data of the occupant exists in the preset database, the step S241 includes: according to the face characteristic values and the stored characteristic values of a plurality of stored face data in a preset database, determining the face phase difference corresponding to each stored face data through a preset phase difference formula;
the preset phase difference formula is as follows:
wherein d (x, y (j)) is the phase difference; a is the number of face feature values; n is the number of stored face data; x is x i The i-th face feature value; y (j) i The ith face feature value of the face data is stored for the jth.
In a specific implementation, assuming that a preset phase difference threshold value is 0.9, 3 face data are stored in a preset database, 128 face feature values generated by a ResNet18 network and 128 feature values corresponding to each face data are input into a preset phase difference formula, and the obtained face phase differences corresponding to the 3 face data are respectively: 0.5, 0.95, and 0.8, it is determined that the second face data matches the face data of the occupant, seat data corresponding to the second face data is read, and the vehicle seat is adjusted based on the seat data.
According to the face feature values and the storage sign values of a plurality of stored face data in a preset database, the face phase difference degree corresponding to each stored face data is determined; and if the face phase difference degree is larger than or equal to a preset phase difference degree threshold value, judging that the face data of the passengers exist in the preset database. According to the method and the device, when the face phase difference is larger than or equal to the preset phase difference threshold value, the face data of the preset database on the face of the passenger is judged, and the adjustment efficiency of the vehicle seat is improved.
Referring to fig. 8, fig. 8 is a flowchart illustrating a third embodiment of the automatic adjustment method for a vehicle seat according to the present invention.
Based on the foregoing embodiments, in this embodiment, after step S2, the method further includes:
step S201: and acquiring physical characteristic data of the passengers when the facial data of the passengers do not exist in the preset database.
It is to be understood that when the face data of the occupant does not exist in the preset database, it is determined that the seat data matching the occupant does not exist in the preset database; the physical characteristic data may be data representing physical characteristics of the occupant, and the physical characteristic data may be determined from pressure data acquired by the seat sensor.
Step S202: and inputting the physical characteristic data into a second preset convolutional neural network model, and acquiring fitting seat data output by the second preset convolutional neural network model.
It can be understood that the second preset convolutional neural network model may be a preset trained convolutional neural network model, and the second preset convolutional neural network model in this embodiment may be a trained res net18 convolutional neural network model, and in this embodiment, the network structure of res net18 may refer to fig. 5, and in this embodiment, x=3 of the output layer; the fitting seat data may be seat data output by the second preset convolutional neural network mode according to the body characteristic data, and the seat data includes cushion data, backrest data and headrest data.
Step S203: and automatically adjusting the vehicle seat corresponding to the passenger according to the fitting seat data.
In specific implementation, when no face data of an occupant exists in a preset database, pressure data of the occupant is acquired through a seat sensor, physical characteristic data of the occupant is determined according to the pressure data, the physical characteristic data is input into a pre-trained ResNet18, the ResNet18 fits cushion data, backrest data and headrest data suitable for the occupant according to the physical characteristic data, a control unit controls a cushion adjusting unit to adjust the front and rear positions of a cushion of a vehicle seat according to the cushion data, controls a backrest adjusting unit to adjust the backrest inclination angle of the vehicle seat according to the backrest data, and controls a headrest adjusting unit to adjust the upper and lower positions of a headrest according to the headrest data.
Further, in order to determine physical characteristic data of the occupant, the step S201 includes: when the face data of the passengers do not exist in the preset database, acquiring pressure data, which are acquired through a seat sensor, of the corresponding vehicle seats of the passengers, wherein the pressure data comprise headrest pressure, backrest pressure and cushion pressure; according to the pressure data, determining physical characteristic data of the passengers through a preset conversion formula; the preset conversion formula is as follows:
R＝α*H+β*(B+S)
wherein R is a body characteristic value; alpha is a first weight coefficient; h is the headrest pressure; beta is a second weight coefficient; b is the back pressure; s is cushion pressure.
It is to be understood that the seat sensor may be a pressure sensor provided on a vehicle seat, the seat sensor including a cushion sensor that collects cushion pressure, a backrest sensor that collects backrest pressure, and a headrest sensor that collects headrest pressure; the first weight coefficient and the second weight coefficient are constants greater than 0 and less than 1, and may be preset, for example, the first weight coefficient may be set to 0.2 and the second weight coefficient may be set to 0.8.
In specific implementation, when no face data of an occupant exists in a preset database, cushion pressure is acquired through a cushion sensor, backrest pressure is acquired through a backrest sensor, headrest pressure is acquired through a headrest sensor, the cushion pressure, the backrest pressure and the headrest pressure are input into a preset conversion formula to obtain body characteristic values of the occupant, and the body characteristic values are input into a pre-trained ResNet18 network to obtain fitting seat data.
Further, in order to improve the adjustment efficiency of the vehicle seat, after the vehicle seat corresponding to the occupant is automatically adjusted according to the fitting seat data, the method further includes: and when the adjustment of the vehicle seat is completed, storing the facial data and the fitting seat data into the preset database correspondingly.
In the embodiment, when the facial data of the passengers do not exist in the preset database, the physical characteristic data of the passengers are obtained; inputting the physical characteristic data into a second preset convolutional neural network model, and acquiring fitting seat data output by the second preset convolutional neural network model; and automatically adjusting the vehicle seat corresponding to the passenger according to the fitting seat data. According to the embodiment, when the face data of the passengers does not exist in the preset database, the body characteristic data of the passengers are input into the second preset convolutional neural network to obtain fitting seat data, the vehicle seats are adjusted according to the fitting seat data, and when the passengers drive or ride the vehicle for the first time, the vehicle seats can still be automatically adjusted, so that user experience is improved.
In addition, the embodiment of the invention also provides a storage medium, wherein the storage medium stores a vehicle seat automatic adjusting program, and the vehicle seat automatic adjusting program realizes the steps of the vehicle seat automatic adjusting method when being executed by a processor.
Referring to fig. 9, fig. 9 is a block diagram showing the construction of a first embodiment of the automatic vehicle seat adjusting apparatus of the present invention.
As shown in fig. 9, the vehicle seat automatic adjustment device according to the embodiment of the present invention includes:
an image acquisition module 10, configured to acquire an image of an occupant through a vehicle-mounted camera when the occupant is detected in a vehicle cabin;
the determining module 20 is configured to perform face recognition on a face image in the image, and determine whether face data of the occupant exists in a preset database according to a result of the face recognition;
a data acquisition module 30, configured to acquire seat data corresponding to face data of the occupant from the preset database when the face data of the occupant exists in the preset database;
and the adjusting module 40 is used for automatically adjusting the vehicle seat corresponding to the passenger according to the seat data.
When an occupant is detected in a carriage, the image of the occupant is acquired through a vehicle-mounted camera; carrying out face recognition on the face image in the image, and determining whether the face data of the passengers exist in a preset database according to the result of the face recognition; when the face data of the passengers exist in the preset database, acquiring seat data corresponding to the face data from the preset database; and automatically adjusting the vehicle seat corresponding to the passenger according to the seat data. Because the face image in the passenger image is recognized when the passenger is detected in the carriage, the seat data corresponding to the face data is acquired when the face data of the passenger exists in the preset database according to the recognition result, and the vehicle seat is automatically adjusted according to the seat data, the technical problem that the vehicle seat is required to be manually adjusted in the prior art is solved, and the adjustment efficiency is improved while the adjustment accuracy of the seat is improved.
Based on the above-described first embodiment of the vehicle seat automatic adjusting device of the present invention, a second embodiment of the vehicle seat automatic adjusting device of the present invention is proposed.
In this embodiment, the determining module 20 is further configured to classify the image according to a preset classification mode, and determine whether a face image exists in the image according to a classification result; when a face image exists in the image, carrying out face recognition on the face image through a first preset convolutional neural network model; determining a face characteristic value of the face image according to a face recognition result; and determining whether the face data of the passengers exist in a preset database according to the face characteristic values.
The determining module 20 is further configured to determine a face phase difference degree corresponding to each stored face data according to the face feature value and the stored sign values of the plurality of stored face data in the preset database; and if the face phase difference degree is larger than or equal to a preset phase difference degree threshold value, judging that the face data of the passengers exist in the preset database.
The determining module 20 is further configured to determine, according to the face feature value and the stored feature values of the plurality of stored face data in the preset database, a face phase difference corresponding to each stored face data according to a preset phase difference formula; the preset phase difference formula is as follows:
wherein d (x, y (j)) is the phase difference; a is the number of face feature values; n is the number of stored face data; x is x i The i-th face feature value; y (j) i The ith face feature value of the face data is stored for the jth.
The data obtaining module 30 is further configured to obtain physical characteristic data of the occupant when no facial data of the occupant exists in the preset database; inputting the physical characteristic data into a second preset convolutional neural network model, and acquiring fitting seat data output by the second preset convolutional neural network model; and automatically adjusting the vehicle seat corresponding to the passenger according to the fitting seat data.
The data acquisition module 30 is further configured to acquire pressure data of the passenger corresponding to the vehicle seat acquired by the seat sensor when no face data of the passenger exists in the preset database, where the pressure data includes a headrest pressure, a backrest pressure, and a cushion pressure; according to the pressure data, determining physical characteristic data of the passengers through a preset conversion formula; the preset conversion formula is as follows:
R＝α*H+β*(B+S)
wherein R is a body characteristic value; alpha is a first weight coefficient; h is the headrest pressure; beta is a second weight coefficient; b is the back pressure; s is cushion pressure.
The data acquisition module 30 is further configured to store the face data and the fitting seat data to the preset database when the adjustment of the vehicle seat is completed.
Other embodiments or specific implementation manners of the automatic adjustment device for a vehicle seat according to the present invention may refer to the above method embodiments, and will not be described herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. read-only memory/random-access memory, magnetic disk, optical disk), comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (7)
1. A method of automatically adjusting a vehicle seat, the method comprising:
when a passenger is detected in a carriage, acquiring an image of the passenger through a vehicle-mounted camera;
carrying out face recognition on the face image in the image, and determining whether the face data of the passengers exist in a preset database according to the result of the face recognition;
when the face data of the passengers exist in the preset database, acquiring seat data corresponding to the face data from the preset database;
automatically adjusting a vehicle seat corresponding to the passenger according to the seat data;
the step of carrying out face recognition on the face image in the image and determining whether the face data of the passengers exist in a preset database according to the result of the face recognition comprises the following steps:
classifying the images through a preset classification mode, and determining whether a face image exists in the images according to classification results;
when a face image exists in the image, carrying out face recognition on the face image through a first preset convolutional neural network model;
determining a face characteristic value of the face image according to a face recognition result;
determining whether the face data of the passengers exist in a preset database according to the face characteristic values;
the step of determining whether the face data of the passengers exist in a preset database according to the face feature value comprises the following steps:
determining the face phase difference degree corresponding to each stored face data according to the face characteristic value and the stored sign values of a plurality of stored face data in a preset database;
if the face phase difference degree is larger than or equal to a preset phase difference degree threshold value, judging that the face data of the passengers exist in the preset database;
the step of determining the face phase difference degree corresponding to each stored face data according to the face characteristic value and the stored sign values of a plurality of stored face data in a preset database comprises the following steps:
according to the face characteristic values and the stored characteristic values of a plurality of stored face data in a preset database, determining the face phase difference corresponding to each stored face data through a preset phase difference formula;
the preset phase difference formula is as follows:
wherein d (x, y (j)) is the phase difference; a is the number of face feature values; n is the number of stored face data; x is x i The i-th face feature value; y (j) i The ith face feature value of the face data is stored for the jth.
2. The method of claim 1, wherein after performing face recognition on the face image in the image and determining whether the face data of the occupant exists in the preset database according to the result of the face recognition, further comprises:
acquiring physical characteristic data of the passengers when the facial data of the passengers do not exist in the preset database;
inputting the physical characteristic data into a second preset convolutional neural network model, and acquiring fitting seat data output by the second preset convolutional neural network model;
and automatically adjusting the vehicle seat corresponding to the passenger according to the fitting seat data.
3. The method of claim 2, wherein the acquiring physical characteristic data of the occupant when no facial data of the occupant is present in the preset database comprises:
when the face data of the passengers do not exist in the preset database, acquiring pressure data, which are acquired through a seat sensor, of the corresponding vehicle seats of the passengers, wherein the pressure data comprise headrest pressure, backrest pressure and cushion pressure;
according to the pressure data, determining physical characteristic data of the passengers through a preset conversion formula;
the preset conversion formula is as follows:
R＝α*H+β*(B+S)
wherein R is a body characteristic value; alpha is a first weight coefficient; h is the headrest pressure; beta is a second weight coefficient; b is the back pressure; s is cushion pressure.
4. The method of claim 2, wherein said automatically adjusting said occupant's corresponding vehicle seat based on said fitted seat data further comprises:
and when the adjustment of the vehicle seat is completed, storing the facial data and the fitting seat data into the preset database correspondingly.
5. An automatic vehicle seat adjustment device, characterized in that it comprises:
the image acquisition module is used for acquiring images of passengers through the vehicle-mounted camera when the passengers are detected in the carriage;
the determining module is used for carrying out face recognition on the face image in the image and determining whether the face data of the passengers exist in a preset database according to the result of the face recognition;
the data acquisition module is used for acquiring seat data corresponding to the face data from the preset database when the face data of the passengers exist in the preset database;
the adjusting module is used for automatically adjusting the vehicle seat corresponding to the passenger according to the seat data;
the determining module is further used for classifying the images through a preset classifying mode, and determining whether face images exist in the images according to classifying results; when a face image exists in the image, carrying out face recognition on the face image through a first preset convolutional neural network model; determining a face characteristic value of the face image according to a face recognition result; determining whether the face data of the passengers exist in a preset database according to the face characteristic values;
the determining module is further configured to determine a face phase difference degree corresponding to each stored face data according to the face feature value and the stored sign values of the plurality of stored face data in the preset database; if the face phase difference degree is larger than or equal to a preset phase difference degree threshold value, judging that the face data of the passengers exist in the preset database;
the determining module is further configured to determine a face phase difference degree corresponding to each stored face data according to the face feature value and a preset phase difference degree formula according to the stored feature values of the plurality of stored face data in the preset database;
the preset phase difference formula is as follows:
wherein d (x, y (j)) is the phase difference; a is the number of face feature values; n is the number of stored face data; x is x i The i-th face feature value; y (j) i The ith face feature value of the face data is stored for the jth.
6. An automatic vehicle seat adjustment apparatus, characterized in that the apparatus comprises: a memory, a processor, and a vehicle seat automatic adjustment program stored on the memory and executable on the processor, the vehicle seat automatic adjustment program configured to implement the steps of the vehicle seat automatic adjustment method according to any one of claims 1 to 4.
7. A storage medium having stored thereon a vehicle seat automatic adjustment program which, when executed by a processor, implements the steps of the vehicle seat automatic adjustment method according to any one of claims 1 to 4.
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