CN108596042A - Enabling control method and system - Google Patents
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- CN108596042A CN108596042A CN201810271388.7A CN201810271388A CN108596042A CN 108596042 A CN108596042 A CN 108596042A CN 201810271388 A CN201810271388 A CN 201810271388A CN 108596042 A CN108596042 A CN 108596042A
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- 238000013527 convolutional neural network Methods 0.000 claims abstract description 39
- 210000000746 body region Anatomy 0.000 claims abstract description 24
- 238000011478 gradient descent method Methods 0.000 claims description 5
- 238000000605 extraction Methods 0.000 abstract 1
- 238000013475 authorization Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
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Abstract
The invention discloses a kind of enabling control method and systems, after obtaining human body region of interest area image, calculate Human Height based on human body region of interest area image, on the one hand judge whether human body is children based on Human Height；On the other hand human body classification is carried out to human body region of interest area image using convolutional neural networks model；Door body is controlled when the human body classification results based on Human Height judging result, and based on convolutional neural networks model are children and keeps locking, is otherwise controlled door body and is unlocked；The mode that this combination height judgement+age identification judges, the crowd of non-children can be excluded from height by height judgement, the crowd of non-children is clearly excluded by way of judging classification age-related characteristic values such as apparel characteristic, figure and features feature, height feature, the fat or thin features of convolutional neural networks extraction people from the age, so that classification results more closing to reality is more accurate, it can prevent children from arbitrarily opening door body, avoid potential danger.
Description
Technical field
The invention belongs to image procossing identification technology fields, specifically, being to be related to a kind of enabling control method and system.
Background technology
Popularizing for cabinet, household electrical appliance with door body etc. is provided convenience for user's life, but there is also certain safety
Hidden danger；For example, filling up the cabinet of article, refrigerator etc. when opening door body, there are the possibility that article is fallen, if youngster at this time
Child opens door body, then the article that may be dropped accidentally injures head etc.；Alternatively, equal door body of opening pierces cabinet to children out of curiosity
In, other injury accidents as accidental cause can't get out or occur.
In order to avoid the potential injury to children, in the prior art, there are some enabling control methods, for example, with infrared
The height of human body occurred before sensor detection door body, when height determines threshold value less than a height, then at the lock that controls door body
In locking state, child is prevented, but relatively difficult for the setting of height threshold value in this scheme, it is right when setting relatively low
Door body can not be then opened in the shorter adult of height, setting is then unfavorable for protecting children when higher；In another example being obtained using camera
The face of user is identified in the mode for taking family facial information, and unauthorized user can not open door body or authorized user
Door body can be opened, but this mode needs the facial information of typing user, setting authorization message etc. in advance, occupying system resources
And it is inconvenient to use.
Invention content
This application provides a kind of enabling control method and systems, and it is accurate and efficient right can be based on user's human body information
User classifies, and prevents children from arbitrarily opening door body, avoids potential danger.
In order to solve the above technical problems, the application is achieved using following technical scheme：
It is proposed a kind of enabling control method, including：Obtain human body region of interest area image；Based on the human body area-of-interest figure
As calculating Human Height；Judge whether human body is children based on the Human Height；And use convolutional neural networks model pair
The human body region of interest area image carries out human body classification；Judge to be based on the Human Height judging result, and based on described
Whether the human body classification results of convolutional neural networks model are children, if so, control door body keeps locking, otherwise control door body
It unlocks.
Further, the convolutional neural networks model includes three convolutional layers, two full articulamentums and a grader,
People's is classified as children and non-children corresponding to the grader output human body.
Further, the convolutional neural networks model can be based on the update of offline or online mode.
Further, the convolutional neural networks are trained using stochastic gradient descent method.
It proposes a kind of enabling control system, including door body, further includes camera unit, the first taxon, the second grouping sheet
Member, judging unit and control unit；The camera unit, for obtaining human body region of interest area image；First grouping sheet
Whether member for calculating Human Height based on the human body region of interest area image, and judges human body based on the Human Height
For children；Second taxon, for being carried out to the human body region of interest area image using convolutional neural networks module
Human body is classified；The judging unit is based on the Human Height judging result for judging, and is based on the convolutional Neural net
Whether the human body classification results of network model are children；If so, control unit control door body keeps locking, otherwise, control is single
Member control door body is unlocked.
Further, the convolutional neural networks model includes three convolutional layers, two full articulamentums and a grader,
People's is classified as children and non-children corresponding to the grader output human body.
Further, the system also includes storage units and updating unit；The storage unit, for storing the volume
Product neural network model；The updating unit, for to the convolutional neural networks model be based on offline or online mode into
Row update.
Further, the convolutional neural networks are trained using stochastic gradient descent method.
Compared with prior art, the advantages of the application and good effect is：The enabling control method and be that the application proposes
In system, the human body region of interest area image of the user occurred before door body is obtained using camera unit, on the one hand, emerging based on human body sense
Interesting area image judges the height of human body, judges whether active user is children by Human Height, on the other hand, will obtain
Human body region of interest area image input trained convolutional neural networks model and carry out human body classification, classification output be children or
Non- children are being judged by height and are judging, when human body classification is children, to control door body by convolutional neural networks
Keep locking, and when any one of its is judged as non-children if control door body unlocking so that the crowd of the non-children mankind can
Door body is opened to exert a force after being unlocked；It is this using the body of entire people as area-of-interest, sentence in conjunction with height judgement+age identification
Disconnected mode can be judged the crowd that can exclude non-children from height by height, all of people is extracted by convolutional neural networks
Such as apparel characteristic, figure and features feature, height feature, the age-related characteristic value of fat or thin feature judge the mode of classification from year
The crowd of non-children is clearly excluded on age so that classification results more closing to reality is more accurate, and shifts to an earlier date typing without user
Authorization message is a kind of accurately and efficiently mode classification, can prevent children from arbitrarily opening door body, avoid potential danger.
After the detailed description of the application embodiment is read in conjunction with the figure, other features and advantages of the application will become more
Add clear.
Description of the drawings
Fig. 1 is the flow chart for the enabling control method that the application proposes；
Fig. 2 is the system block diagram for the enabling control system that the application proposes.
Specific implementation mode
The specific implementation mode of the application is described in more detail below in conjunction with the accompanying drawings.
The enabling control method that the application proposes, as shown in Figure 1, including：
Step S11:Obtain human body region of interest area image.
Camera unit is nearby set in door body, door body of refrigerator of cabinet etc. so that camera unit can obtain people
Body-sensing interest area image, specifically, the pedestrian detection method based on Yolo, which may be used, obtains human body area-of-interest.
Step S12：Human Height is calculated based on human body region of interest area image.
Specifically, human body rectangle frame interested is obtained using the pedestrian detection method based on Yolo, calculates detection rectangle frame
Length, unit is pixel number, according to image pixel number demarcate in advance with the distance calculating correspondence under reality scene
Calculate the height of user.
Step S13：Judge whether human body is children based on Human Height.Meanwhile
Step S14：Human body classification is carried out to the human body region of interest area image using convolutional neural networks model.
Human body region of interest area image is sent in trained convolutional neural networks model, specifically, convolutional Neural net
Network model includes three convolutional layers, two full articulamentums and a grader, and first layer convolutional layer uses 96 convolution kernels, each
Convolution kernel number of parameters is, convolution step-length is 4, and activation primitive uses Relu, pondization to use maximum value pond, pond
The size of change is selected, pond step-length stride selections 2；Second layer convolutional layer uses 256 convolution kernels, convolution kernel size
For, convolution step-length is 1, and the size in pond is selected, pond step-length stride selections 2；Third layer convolutional layer uses
384 filters, convolution kernel size are；First full articulamentum neuron number selection 512, second full articulamentum god
512 are also selected through first number, output layer is set as two：Children and the grader of non-children namely convolutional neural networks model
People's is classified as children or non-children corresponding to output human body.
For convolutional neural networks model in training, weights initialisation method use standard deviation for 0.01, mean value for 0 Gauss
Just it is distributed very much；Prevent over-fitting from using two kinds of strategies in training：1, using dropout methods, dropout ratios use 0.5；2、
Data extending, data extending is taken to pass through inputPicture, then carry out random cropping, be cut toPicture, cutting carried out based on human body center；Training method uses stochastic gradient descent method, mini-
Batch sizes selection 50, learning rate is adjusted to 0.0001 by learning rate size 0.001 then after iterating to 10000 times.
It should be noted that in above-mentioned the embodiment of the present application, do not limit step S14 and step S12 executes sequence.
Step S15：Judge to be based on Human Height judging result, and the classification knot of the human body based on convolutional neural networks model
Whether fruit is children；If so, step S16：It controls door body and keeps locking, otherwise step S17：Door body is controlled to unlock.
In the embodiment of the present application, convolutional neural networks model can be based on the update of offline or online mode.
In the enabling control method that above-mentioned the application proposes, the human body of the user occurred before door body is obtained using camera unit
Region of interest area image, on the one hand, the height that human body is judged based on human body region of interest area image is judged by Human Height
Whether active user is children, on the other hand, the human body region of interest area image of acquisition is inputted trained convolutional Neural net
Network model carries out human body classification, and classification output is children or non-children, and convolutional neural networks are being judged and passed through by height
Judge human body classification be children when, to control door body keep locking, and when any one of its is judged as non-children if
It controls door body to unlock so that the crowd of the non-children mankind can exert a force after being unlocked opens door body；This body with entire people
It in conjunction with the mode that height judgement+age identification judges can judge that non-youngster can be excluded from height by height for area-of-interest
Virgin crowd, extracted by convolutional neural networks apparel characteristic, figure and features feature, height feature, fat or thin feature of people etc. with
Age-dependent characteristic value judges that the mode of classification clearly excludes the crowd of non-children from the age so that classification results are more
Closing to reality is more accurate, and shifts to an earlier date typing authorization message without user, is a kind of accurately and efficiently mode classification, Neng Goufang
Only children arbitrarily open door body, avoid potential danger.
Based on enabling control method set forth above, the application also proposes a kind of enabling control system, as shown in Fig. 2, packet
Include door body 21, camera unit 22, the first taxon 23, the second taxon 24, judging unit 25 and control unit 26；Camera shooting
Unit 22 is for obtaining the human body region of interest area image before door body 21；First taxon 23, for interested based on human body
Area image calculates Human Height, and judges whether human body is children based on Human Height；Second taxon 24 is for using
Convolutional neural networks module carries out human body classification to human body region of interest area image；Judging unit 25 is based on human body body for judging
Whether high judging result, and the human body classification results based on convolutional neural networks model are children；If so, control unit
26 control door body keep locking, and otherwise, control unit 26 controls door body and unlocks.
Specifically, convolutional neural networks model includes three convolutional layers, two full articulamentums and a grader, grader
People's is classified as children and non-children corresponding to output human body.
The system further includes storage unit 27 and updating unit 28；Storage unit 27 is for storing convolutional neural networks mould
Type；Updating unit 28 is then used to be updated to be based on offline or online mode to convolutional neural networks model.
Structure, training and the application of specific convolutional neural networks model are described in detail in above-mentioned enabling control method,
It will not go into details herein.
The enabling control method and system that above-mentioned the application proposes, the body based on entire people are area-of-interest, in conjunction with
The mode that height judgement+age identification judges can be judged the crowd that can exclude non-children from height by height, pass through convolution
Neural network is extracted the age-related characteristic values such as apparel characteristic, figure and features feature, height feature, the fat or thin feature of people and is come
Judging that the mode of classification clearly excludes the crowd of non-children from the age so that classification results more closing to reality is more accurate,
And shift to an earlier date typing authorization message without user, it is a kind of accurately and efficiently mode classification, can prevents children from arbitrarily opening door
Body avoids potential danger.
It should be noted that it is limitation of the present invention that above description, which is not, the present invention is also not limited to the example above,
The variations, modifications, additions or substitutions that those skilled in the art are made in the essential scope of the present invention, are also answered
It belongs to the scope of protection of the present invention.
Claims (8)
1. enabling control method, which is characterized in that including：
Obtain human body region of interest area image；
Human Height is calculated based on the human body region of interest area image；
Judge whether human body is children based on the Human Height；And
Human body classification is carried out to the human body region of interest area image using convolutional neural networks model；
Judge to be based on the Human Height judging result, and the human body classification results based on the convolutional neural networks model are
No is children；If so, control door body keeps locking, otherwise controls door body and unlock.
2. enabling control method according to claim 1, which is characterized in that the convolutional neural networks model includes three
Convolutional layer, two full articulamentums and a grader, people's corresponding to grader output human body is classified as children and non-
It is virgin.
3. enabling control method according to claim 1, which is characterized in that the convolutional neural networks model can be based on from
Line or online mode update.
4. enabling control method according to claim 1, which is characterized in that the convolutional neural networks use stochastic gradient
Descent method is trained.
5. enabling control system, including door body, which is characterized in that further include camera unit, the first taxon, the second grouping sheet
Member, judging unit and control unit；
The camera unit, for obtaining human body region of interest area image；
First taxon for calculating Human Height based on the human body region of interest area image, and is based on the people
Body height judges whether human body is children；
Second taxon, for carrying out human body to the human body region of interest area image using convolutional neural networks module
Classification；
The judging unit is based on the Human Height judging result for judging, and is based on the convolutional neural networks mould
Whether the human body classification results of type are children；If so, control unit control door body keeps locking, otherwise, control unit control
Door body processed is unlocked.
6. enabling control system according to claim 5, which is characterized in that the convolutional neural networks model includes three
Convolutional layer, two full articulamentums and a grader, people's corresponding to grader output human body is classified as children and non-
It is virgin.
7. enabling control system according to claim 5, which is characterized in that the system also includes storage units and update
Unit；
The storage unit, for storing the convolutional neural networks model；The updating unit, for to convolution god
Offline or online mode is based on through network model to be updated.
8. enabling control system according to claim 5, which is characterized in that the convolutional neural networks use stochastic gradient
Descent method is trained.
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| CN201810271388.7A CN108596042A (en) | 2018-03-29 | 2018-03-29 | Enabling control method and system |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109711289A (en) * | 2018-12-13 | 2019-05-03 | Oppo广东移动通信有限公司 | Based reminding method, device, electronic equipment and storage medium by bus |
| CN110298949A (en) * | 2019-07-08 | 2019-10-01 | 珠海格力电器股份有限公司 | A kind of control method for door lock, device, storage medium and door lock |
| CN111311786A (en) * | 2018-11-23 | 2020-06-19 | 杭州眼云智家科技有限公司 | Intelligent door lock system and intelligent door lock control method thereof |
| CN111339873A (en) * | 2020-02-18 | 2020-06-26 | 南京甄视智能科技有限公司 | Passenger flow statistical method and device, storage medium and computing equipment |
| CN112084817A (en) * | 2019-06-13 | 2020-12-15 | 杭州海康威视数字技术股份有限公司 | Detection method and device for children staying in vehicle independently and infrared camera |
| CN112943005A (en) * | 2021-02-01 | 2021-06-11 | 中国第一汽车股份有限公司 | Method and device for controlling locking of back door |
| CN113366507A (en) * | 2018-12-21 | 2021-09-07 | 伟摩有限责任公司 | Training a classifier to detect open vehicle doors |
| CN113585878A (en) * | 2021-08-17 | 2021-11-02 | 珠海格力电器股份有限公司 | Door lock device, control method and equipment |
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| CN111311786A (en) * | 2018-11-23 | 2020-06-19 | 杭州眼云智家科技有限公司 | Intelligent door lock system and intelligent door lock control method thereof |
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| CN111339873A (en) * | 2020-02-18 | 2020-06-26 | 南京甄视智能科技有限公司 | Passenger flow statistical method and device, storage medium and computing equipment |
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| CN112943005A (en) * | 2021-02-01 | 2021-06-11 | 中国第一汽车股份有限公司 | Method and device for controlling locking of back door |
| CN113585878A (en) * | 2021-08-17 | 2021-11-02 | 珠海格力电器股份有限公司 | Door lock device, control method and equipment |
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