CN110370996A - Intelligent automobile electric chair - Google Patents
Intelligent automobile electric chair Download PDFInfo
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- CN110370996A CN110370996A CN201910633014.XA CN201910633014A CN110370996A CN 110370996 A CN110370996 A CN 110370996A CN 201910633014 A CN201910633014 A CN 201910633014A CN 110370996 A CN110370996 A CN 110370996A
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- 239000002245 particle Substances 0.000 claims description 79
- 230000001815 facial effect Effects 0.000 claims description 21
- 230000007246 mechanism Effects 0.000 claims description 15
- 238000000034 method Methods 0.000 claims description 8
- 230000001105 regulatory effect Effects 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000011218 segmentation Effects 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 2
- 238000003709 image segmentation Methods 0.000 claims description 2
- 241000208340 Araliaceae Species 0.000 claims 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 claims 1
- 235000003140 Panax quinquefolius Nutrition 0.000 claims 1
- 235000008434 ginseng Nutrition 0.000 claims 1
- 238000005316 response function Methods 0.000 claims 1
- 230000009286 beneficial effect Effects 0.000 abstract 1
- 230000006870 function Effects 0.000 description 4
- 201000004569 Blindness Diseases 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006386 memory function Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
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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
-
- 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
-
- 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
-
- 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
- B60N2/04—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable the whole seat being movable
- B60N2/06—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable the whole seat being movable slidable
-
- 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
- B60N2/04—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable the whole seat being movable
- B60N2/16—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable the whole seat being movable height-adjustable
-
- 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
- B60N2/22—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable the back-rest being adjustable
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06V40/168—Feature extraction; Face representation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30196—Human being; Person
- G06T2207/30201—Face
Abstract
Intelligent automobile electric chair, including automatic seat, seat sensing module, front end camera, intelligence control system, seat control system, seat regulator and data obtaining module.The invention has the following beneficial effects: providing a kind of Intelligent automobile electric chair, face recognition technology and intelligent control technology are wanted to combine, the automation from main memory and automatic seat for realizing automatic seat is adjusted, and is not necessarily to manual intervention.
Description
Technical field
The invention is related to automatic seat field, and in particular to a kind of Intelligent automobile electric chair.
Background technique
Since the 1980s, automobile is rapidly developed in Chinese market, in people's daily life, automobile
Have become the indispensable important vehicles.Currently, the competition of Chinese automobile market is more and more fierce, and people are to running car
Safety and stationarity require also higher and higher, important component one of of the automotive seat as automobile fitting, convenience and comfortably
Property be often related to the visual field of driver, experience and the state of mind.Good driving sitting position can make driver obtain best view
Open country is easy to manipulation direction plate, pedal, gear lever etc., the seating angle for obtaining the most comfortable and being most accustomed to, as the different user of height
When using same vehicle, it is necessary to readjust seat, therefore, a automotive seat that can be realized memory function urgently emerges.
Summary of the invention
In view of the above-mentioned problems, the present invention is intended to provide a kind of Intelligent automobile electric chair.
The purpose of the invention is achieved through the following technical solutions:
Intelligent automobile electric chair, including automatic seat, seat sensing module, front end camera, intelligence control system, seat
Chair control system, seat regulator and data obtaining module, when seat sensing module detects someone on automatic seat, i.e.,
Front end camera is enabled to acquire facial image, intelligence control system is handled and is identified to the facial image collected, and with
The facial image stored in database is matched, and after successful match, the adjustment parameter of corresponding automatic seat is sent to
Seat control system controls seat regulator according to the adjustment parameter by seat control system and adjusts to automatic seat
Section, when face matching is unsuccessful, whether seat sensing module detection automatic seat is moved, and is moved when detecting that automatic seat exists
When dynamic, even data obtaining module obtains the adjustment parameter of automatic seat, and the adjustment parameter collected is sent to intelligence
The adjustment parameter received and corresponding facial image are stored in database by control system, intelligence control system.
The invention the utility model has the advantages that provide a kind of Intelligent automobile electric chair, face recognition technology and intelligence are controlled
Technology processed is wanted to combine, and the automation from main memory and automatic seat for realizing automatic seat is adjusted, and is not necessarily to manual intervention.
Detailed description of the invention
Innovation and creation are described further using attached drawing, but the embodiment in attached drawing does not constitute and appoints to the invention
What is limited, for those of ordinary skill in the art, without creative efforts, can also be according to the following drawings
Obtain other attached drawings.
Fig. 1 is schematic structural view of the invention；
Fig. 2 is seat regulator structural schematic diagram.
Appended drawing reference:
Automatic seat 1；Seat sensing module 2；Front end camera 3；Intelligence control system 4；Seat control system 5；Seat
Regulating mechanism 6；Data obtaining module 7；Seat height adjusting mechanism 61；Regulating mechanism 62 before and after seat；Backrest adjusts machine
Structure 63.
Specific embodiment
The invention will be further described with the following Examples.
Referring to Fig. 1 and Fig. 2, the Intelligent automobile electric chair of the present embodiment, including automatic seat 1, seat sensing module 2,
Front end camera 3, intelligence control system 4, seat control system 5, seat regulator 6 and data obtaining module 7, when seat sense
When knowing that module 2 detects someone on automatic seat 1, even front end camera 3 acquires facial image, intelligence control system 4 is to adopting
Collect obtained facial image to be handled and identified, and matched with the facial image stored in database, works as successful match
Afterwards, the adjustment parameter of corresponding automatic seat 1 is sent to seat control system 5, by seat control system 5 according to the adjusting
Automatic seat 1 is adjusted in state modulator seat regulator 6, and when face matching is unsuccessful, seat sensing module 2 is examined
Survey whether automatic seat 1 moves, when detecting that automatic seat 1 deposits when moving, even data obtaining module 7 obtains automatic seat
1 adjustment parameter, and the adjustment parameter collected is sent to intelligence control system 4, intelligence control system 4 will receive
Adjustment parameter and corresponding facial image are stored in database.
Preferably, the seat regulator 6 includes regulating mechanism 62 and seat before and after seat height adjusting mechanism 61, seat
Chair backrest adjusting mechanism 63, the seat height adjusting mechanism 61 is for being adjusted the height of automatic seat, the seat
Front and back regulating mechanism 62 is used for being adjusted before and after automatic seat, and the Seat back modulating mechanism 63 is used for electric seat
The backrest of chair is adjusted.
This preferred embodiment provides a kind of Intelligent automobile electric chair, and face recognition technology and intelligent control technology are wanted to tie
It closes, the automation from main memory and automatic seat for realizing automatic seat is adjusted, and is not necessarily to manual intervention.
Preferably, intelligence control system 4 handles the facial image collected, including image denoising processing and figure
As dividing processing, described image denoising is used to remove the noise pollution in the facial image collected, described image point
Processing is cut for carrying out Target Segmentation to the facial image after denoising.
This preferred embodiment is used to carry out denoising and dividing processing to the facial image collected, for the later period
Facial image identification is laid a good foundation.
Preferably, image is carried out to the facial image after denoising using the multi-threshold image segmentation method based on particle group optimizing
Dividing processing determines multi-threshold segmentation algorithm by optimizing using Otsu inter-class variance as the fitness function of particle swarm algorithm
In optimal threshold.
Preferably, in the searching process of particle swarm algorithm, using following manner to the position of particle in particle swarm algorithm
It is updated, specifically includes with step-length:
(1) defining the particle i update probability corresponding in (k+1) secondary iteration isThenCalculation formula
Are as follows:
In formula, O indicates the search dimension of particle swarm algorithm,Indicate particle i corresponding fitness at the kth iteration
Value,Indicate that the fitness value of particle l at the kth iteration, L indicate the population in population；
(2) particle i generates equally distributed random number in [0,1], if the random number generated is corresponding more greater than particle i
New probabilityThe then location update formula of particle i are as follows:
In formula,Indicate the updated position particle i,Indicate the position of particle i when kth time iteration；
(3) particle i generates equally distributed random number in [0,1], if the random number generated is corresponding more less than particle i
New probabilityThen the position of particle i and step-length are updated using following formula:
In formula,Indicate the updated position particle i,Indicate the position of particle i when kth time iteration, Vi k+1It indicates
The step-length of particle i when (k+1) secondary iteration, Vi kIndicate the step-length of particle i when kth time iteration,Indicate that particle i changes in kth time
For when inertia weight, andωstartIndicate that algorithm is initial
Inertia weight value, ωendIndicate the inertia weight value at the end of algorithm, k indicates the current the number of iterations of algorithm, kmaxTable
Show the maximum number of iterations of algorithm,Indicate particle i corresponding fitness value at the kth iteration,Indicate that particle i exists
Corresponding fitness value when (k-1) secondary iteration, c1And c2It is common learning coefficient, and c1And c2For the constant between [1,2],
rand1And rand2For the arbitrary number between [0,1] and therebetween without any relations of dependence, PkIt indicates in kth time iteration
When population in particle position mean value, making particle, into population, the mean value of particle position learns at no point in the update process, compared to biography
It unites the mode for learning particle to individual history optimal value, the mean value of particle position more accurately reflects algorithm in population
Local message avoids algorithm and falls into locally optimal solution, α is to adjust to significantly increase the diversity of particle in algorithm
Parameter, for adjusting the particle weight that particle position mean value learns into population at no point in the update process, andO
Indicate the search dimension of particle swarm algorithm, L indicates the population in population, and adjustment parameter α is tieed up according to the search of particle swarm algorithm
The particle weight that particle position mean value learns into population is adjusted in the population of degree and population, when searching for particle swarm algorithm
When Suo Weidu is larger or population scale is smaller, increase the influence that local message adjusts position, improves the optimizing essence of algorithm
Degree, gkIndicate global optimum when kth time iteration, β is adjustment parameter, the power learnt for adjusting particle to global optimum
Weight, and the calculation formula of β are as follows:
In formula, gjIndicate global optimum when iteration j, g(j-1)Indicate global optimum when (j-1) secondary iteration
Value, h (gj) indicate global optimum gjCorresponding fitness value, h (g(j-1)) indicate global optimum g(j-1)Corresponding fitness
Value, k indicate current iteration number, kmaxMaximum number of iterations is indicated, in the adjustment parameter β, according to subsequent iteration process
The situation of change of the fitness value of middle global optimum judges the optimizing situation of algorithm, when the global optimum of continuous 3 iteration
Fitness value ratio be less than or equal to 1 when, even β=0, avoid particle from falling into globally optimal solution or avoid particle to fitness
It is worth poor direction to evolve, when the mean value of the fitness value ratio of the global optimum of continuous 3 iteration is greater than 1, even β root
Increase the weight that particle learns to globally optimal solution according to the number of iterations.
This preferred embodiment is comprehensive according to the practical optimizing situation of particle and the base of algorithm in the update probability of definition
This parameter adjusts update probability in real time, and the fitness value for introducing particle measures the current optimizing situation of particle, avoids
Blindness adjustment to optimizing situation preferably particle, is adjusted update probability according to the search dimension of particle swarm algorithm, when
When the search dimension of population is larger, that is, reduce the probability of particle adjustment, thus caused by avoiding excessive particle position from adjusting
Increase the runing time of particle swarm algorithm；In addition, this preferred embodiment uses a kind of improved inertia weight function, compared to biography
The inertia weight function of system, the inertia weight function of this preferred embodiment introduce the fitness value variation measure algorithm for updating front and back
The optimizing effect for updating front and back, avoids the influence that unfavorable experience updates particle；When the position to particle is updated,
The update mode of particle step-length is improved, the influence degree of algorithm local message is increased, to increase the more of particle
Sample avoids algorithm from falling into locally optimal solution.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of range is protected, although explaining in detail referring to preferred embodiment to the present invention, those skilled in the art are answered
Work as understanding, it can be with modification or equivalent replacement of the technical solution of the present invention are made, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (5)
1. Intelligent automobile electric chair, characterized in that including automatic seat, seat sensing module, front end camera, intelligent control
System, seat control system, seat regulator and data obtaining module have when seat sensing module detects on automatic seat
When people, even front end camera acquires facial image, intelligence control system is handled and is known to the facial image collected
Not, and with the facial image stored in database it is matched, after successful match, by the adjustment parameter of corresponding automatic seat
Be sent to seat control system, by seat control system according to the adjustment parameter control seat regulator to automatic seat into
Row is adjusted, and when face matching is unsuccessful, whether seat sensing module detection automatic seat is moved, when detecting that automatic seat deposits
When moving, even data obtaining module obtains the adjustment parameter of automatic seat, and the adjustment parameter collected is sent to
The adjustment parameter received and corresponding facial image are stored in database by intelligence control system, intelligence control system.
2. Intelligent automobile electric chair according to claim 1, characterized in that seat regulator includes seat height low-key
Mechanism, seat front and back regulating mechanism and Seat back modulating mechanism, the seat height adjusting mechanism is saved to be used for automatic seat
Height be adjusted, regulating mechanism is used for being adjusted before and after automatic seat before and after the seat, the backrest
Regulating mechanism is for being adjusted the backrest of automatic seat.
3. Intelligent automobile electric chair according to claim 2, characterized in that intelligence control system is to the people collected
Face image is handled, including image denoising processing and image dividing processing, described image denoising are acquired for removing
To facial image in noise pollution, described image dividing processing be used for after denoising facial image carry out Target Segmentation.
4. Intelligent automobile electric chair according to claim 3, characterized in that use the multi-threshold based on particle group optimizing
Image segmentation carries out image dividing processing to the facial image after denoising, using Otsu inter-class variance as the suitable of particle swarm algorithm
Response function determines the optimal threshold in multi-threshold segmentation algorithm by optimizing.
5. Intelligent automobile electric chair according to claim 4, characterized in that in the searching process of particle swarm algorithm,
The position of particle in particle swarm algorithm and step-length are updated using following manner, specifically included:
(1) defining the particle i update probability corresponding in (k+1) secondary iteration isThenCalculation formula are as follows:
In formula, O indicates the search dimension of particle swarm algorithm,Indicate particle i corresponding fitness value at the kth iteration,
Indicate that the fitness value of particle l at the kth iteration, L indicate the population in population；
(2) particle i generates equally distributed random number in [0,1], if the random number generated is greater than, particle i is corresponding to be updated generally
RateThe then location update formula of particle i are as follows:
In formula,Indicate the updated position particle i,Indicate the position of particle i when kth time iteration；
(3) particle i generates equally distributed random number in [0,1], if the random number generated is less than, particle i is corresponding to be updated generally
RateThen the position of particle i and step-length are updated using following formula:
In formula,Indicate the updated position particle i,Indicate the position of particle i when kth time iteration, Vi k+1Indicate (k+
1) when secondary iteration particle i step-length, Vi kIndicate the step-length of particle i when kth time iteration,Indicate particle i at the kth iteration
Inertia weight, andωstartIt indicates that algorithm is initial to be used to
Property weight value, ωendIndicate the inertia weight value at the end of algorithm, k indicates the current the number of iterations of algorithm, kmaxIt indicates to calculate
The maximum number of iterations of method,Indicate particle i corresponding fitness value at the kth iteration,Indicate particle i in (k-
1) corresponding fitness value, c when secondary iteration1And c2For common learning coefficient, and c1And c2For the constant between [1,2], rand1With
rand2For the arbitrary number between [0,1], PkIndicate the mean value of particle position in population at the kth iteration, α is to adjust ginseng
Number, andO indicates the search dimension of particle swarm algorithm, and L indicates the population in population, gkIndicate kth time
Global optimum when iteration, β are adjustment parameter, and the calculation formula of β are as follows:
In formula, gjIndicate global optimum when iteration j, g(j-1)Indicate global optimum when (j-1) secondary iteration, h
(gj) indicate global optimum gjCorresponding fitness value, h (g(j-1)) indicate global optimum g(j-1)Corresponding fitness value, k
Indicate current the number of iterations, kmaxIndicate maximum number of iterations.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910633014.XA CN110370996B (en) | 2019-07-15 | 2019-07-15 | Intelligent electric seat for automobile |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910633014.XA CN110370996B (en) | 2019-07-15 | 2019-07-15 | Intelligent electric seat for automobile |
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| Publication Number | Publication Date |
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| CN110370996A true CN110370996A (en) | 2019-10-25 |
| CN110370996B CN110370996B (en) | 2021-08-31 |
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ID=68253073
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910633014.XA Active CN110370996B (en) | 2019-07-15 | 2019-07-15 | Intelligent electric seat for automobile |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112638703A (en) * | 2020-04-30 | 2021-04-09 | 华为技术有限公司 | Seat adjusting method, device and system |
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Cited By (1)
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|---|---|---|---|---|
| CN112638703A (en) * | 2020-04-30 | 2021-04-09 | 华为技术有限公司 | Seat adjusting method, device and system |
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| CN110370996B (en) | 2021-08-31 |
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