CN109063653A - Image processing method and device - Google Patents
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- CN109063653A CN109063653A CN201810888877.7A CN201810888877A CN109063653A CN 109063653 A CN109063653 A CN 109063653A CN 201810888877 A CN201810888877 A CN 201810888877A CN 109063653 A CN109063653 A CN 109063653A
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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
- G06V40/20—Movements or behaviour, e.g. gesture recognition
- G06V40/28—Recognition of hand or arm movements, e.g. recognition of deaf sign language
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
Abstract
The embodiment of the present application discloses image processing method and device.One specific embodiment of this method includes: the gesture frame for obtaining the image comprising gesture, wherein the gesture frame is used to indicate the position of the included gesture of image；From the image, the topography indicated by the gesture frame comprising gesture is extracted；The topography is inputted into critical point detection model, to determine the key point information of the finger tip of the included gesture of the image, wherein the key point information includes finger tip location information.The embodiment of the present application is able to detect the key point of important finger tip, avoids carrying out detecting caused invalid detection to the very low key point of applying frequency in gesture, improves detection efficiency.
Description
Technical field
The invention relates to field of computer technology, and in particular at Internet technical field more particularly to image
Manage method and apparatus.
Background technique
Gesture interaction is a kind of important form of human-computer interaction, has been widely used.Detection to the key point of gesture,
Usually the dozens of key point in gesture is detected.
In the prior art, detection key point will detect above-mentioned multiple key points every time, however detect this obtained
Often utilization rate is relatively low for many key points in a little key points, is not applied subsequent greatly.
Summary of the invention
The embodiment of the present application proposes image processing method and device.
In a first aspect, the embodiment of the present application provides a kind of image processing method, comprising: obtain the image comprising gesture
Gesture frame, wherein gesture frame is used to indicate the position of the included gesture of image；From image, it is signified to extract gesture frame
The topography comprising gesture shown；Topography is inputted into critical point detection model, to determine the hand of the included gesture of image
The key point information of finger tip, wherein key point information includes finger tip location information.
In some embodiments, key point information further include: for each finger tip of gesture, indicate that the finger tip is
No visible finger tip visual information.
In some embodiments, the gesture frame of the image comprising gesture is obtained, comprising: gestures detection is carried out to image,
To determine the gesture frame of the position of instruction the included gesture of image.
In some embodiments, critical point detection model is convolutional neural networks.
In some embodiments, critical point detection model is obtained by following steps training: training sample set is obtained,
In, the sample that training sample is concentrated includes the topography comprising gesture, and the gesture for being included with each topography
Finger tip location information and finger tip visual information；Using topography as input, by the hand of the included gesture of the topography
As output, training initial key point detection model obtains critical point detection mould for fingertip location information and finger tip visual information
Type.
Second aspect, the embodiment of the present application provide a kind of image processing apparatus, comprising: acquiring unit is configured to obtain
Take the gesture frame of the image comprising gesture, wherein gesture frame is used to indicate the position of the included gesture of image；It extracts single
Member is configured to from image, extracts the topography indicated by gesture frame comprising gesture；Determination unit is configured to
Topography is inputted into critical point detection model, to determine the key point information of the finger tip of the included gesture of image, wherein close
Key point information includes finger tip location information.
In some embodiments, key point information further include: for each finger tip of gesture, indicate that the finger tip is
No visible finger tip visual information.
In some embodiments, acquiring unit is further configured to: gestures detection is carried out to image, to determine instruction figure
As the gesture frame of the position of included gesture.
In some embodiments, critical point detection model is convolutional neural networks.
In some embodiments, critical point detection model is obtained by following steps training: training sample set is obtained,
In, the sample that training sample is concentrated includes the topography comprising gesture, and the gesture for being included with each topography
Finger tip location information and finger tip visual information；Using topography as input, by the hand of the included gesture of the topography
As output, training initial key point detection model obtains critical point detection mould for fingertip location information and finger tip visual information
Type.
The third aspect, the embodiment of the present application provide a kind of electronic equipment, comprising: one or more processors；Storage dress
It sets, for storing one or more programs, when one or more programs are executed by one or more processors, so that one or more
A processor realizes the method such as any embodiment in image processing method.
Fourth aspect, the embodiment of the present application provide a kind of computer readable storage medium, are stored thereon with computer journey
Sequence realizes the method such as any embodiment in image processing method when the program is executed by processor.
Image procossing scheme provided by the embodiments of the present application, firstly, the gesture frame of the image comprising gesture is obtained,
In, gesture frame is used to indicate the position of the included gesture of image.Later, from image, packet indicated by gesture frame is extracted
Topography containing gesture.Then, topography is inputted into critical point detection model, to determine the finger of the included gesture of image
The key point information of point, wherein key point information includes finger tip location information.The embodiment of the present application is able to detect important hand
The key point of finger tip avoids carrying out detecting caused invalid detection to the very low key point of applying frequency in gesture, improves detection
Efficiency.
Detailed description of the invention
By reading a detailed description of non-restrictive embodiments in the light of the attached drawings below, the application's is other
Feature, objects and advantages will become more apparent upon:
Fig. 1 is that this application can be applied to exemplary system architecture figures therein；
Fig. 2 is the flow chart according to one embodiment of the image processing method of the application；
Fig. 3 is the schematic diagram according to an application scenarios of the image processing method of the application；
Fig. 4 is the structural schematic diagram according to one embodiment of the image processing apparatus of the application；
Fig. 5 is adapted for the structural schematic diagram for the computer system for realizing the electronic equipment of the embodiment of the present application.
Specific embodiment
The application is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining related invention, rather than the restriction to the invention.It also should be noted that in order to
Convenient for description, part relevant to related invention is illustrated only in attached drawing.
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.The application is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
Fig. 1 is shown can be using the exemplary system of the embodiment of the image processing method or image processing apparatus of the application
System framework 100.
As shown in Figure 1, system architecture 100 may include terminal device 101,102,103, network 104 and server 105.
Network 104 between terminal device 101,102,103 and server 105 to provide the medium of communication link.Network 104 can be with
Including various connection types, such as wired, wireless communication link or fiber optic cables etc..
User can be used terminal device 101,102,103 and be interacted by network 104 with server 105, to receive or send out
Send message etc..Various telecommunication customer end applications can be installed on terminal device 101,102,103, such as image recognition application,
Short video class application, searching class application, instant messaging tools, mailbox client, social platform software etc..
Here terminal device 101,102,103 can be hardware, be also possible to software.When terminal device 101,102,
103 be hardware when, can be the various electronic equipments with display screen, including but not limited to smart phone, tablet computer, electronics
Book reader, pocket computer on knee and desktop computer etc..It, can be with when terminal device 101,102,103 is software
It is mounted in above-mentioned cited electronic equipment.Multiple softwares or software module may be implemented into (such as providing distribution in it
The multiple softwares or software module of formula service), single software or software module also may be implemented into.It is not specifically limited herein.
Server 105 can be to provide the server of various services, such as provide support to terminal device 101,102,103
Background server.Background server can carry out analyzing etc. to data such as the pictures received processing, and by processing result (example
If image includes the key point information of the finger tip of gesture) feed back to terminal device.
It should be noted that image processing method provided by the embodiment of the present application can be by server 105 or terminal
Equipment 101,102,103 executes, correspondingly, image processing apparatus can be set in server 105 or terminal device 101,
102, in 103.
It should be understood that the number of terminal device, network and server in Fig. 1 is only schematical.According to realization need
It wants, can have any number of terminal device, network and server.
With continued reference to Fig. 2, the process 200 of one embodiment of the image processing method according to the application is shown.The figure
As processing method, comprising the following steps:
Step 201, the gesture frame of the image comprising gesture is obtained, wherein gesture frame is used to indicate image and is included
The position of gesture.
In the present embodiment, executing subject (such as server shown in FIG. 1 or the end of image processing method operation thereon
End equipment) the gesture frame of the image comprising gesture can be obtained from local or other electronic equipments.Gesture frame can be drawn
Gesture is made, to indicate the position of gesture.Specifically, gesture frame can be rectangle.Hand can be used only in above-mentioned executing subject
Gesture frame stores the location information of frame, does not show gesture frame in the picture.In addition, gesture frame can also be intuitively presented
In the picture.Specifically, above-mentioned executing subject can obtain gesture frame using various ways.For example, can from local or
Other electronic equipments obtain predetermined gesture frame.It, can also will be to upper one in addition, if image is the frame in video
Gesture frame of the profile in the region where the gesture frame that frame determines as this frame.
In some optional implementations of the present embodiment, step 201 may include:
Gestures detection is carried out to image, to determine the gesture frame of the position of instruction the included gesture of image.
In these optional implementations, above-mentioned executing subject can carry out gestures detection to above-mentioned image, will test
The frame (Bounding-Box) generated in the process is used as gesture frame.More accurate gesture side can be obtained by detection
Frame.Specifically, image can be inputted to the convolutional neural networks for being able to carry out gestures detection.It can be exported from convolutional neural networks
Gesture frame.
Step 202, from image, the topography indicated by gesture frame comprising gesture is extracted.
In the present embodiment, above-mentioned executing subject can be from above-mentioned image, and extracting includes hand indicated by gesture frame
The topography of gesture.Specifically, above-mentioned executing subject can cut out the topography in gesture frame, hand from above-mentioned image
The sideline of gesture frame and the sideline of topography coincide.
Step 203, topography is inputted into critical point detection model, to determine the pass of the finger tip of the included gesture of image
Key point information, wherein key point information includes finger tip location information.
In the present embodiment, the topography extracted can be inputted critical point detection model by above-mentioned executing subject, be obtained
To the key point information exported from critical point detection model.Key point information is the pass of the finger tip of the included gesture of above-mentioned image
Key point information.Key point information may include finger tip location information.Finger tip location information can be finger tip in Local map
As the coordinate in (or image).
In practice, critical point detection model can be by support vector machines (Support Vector Machine,
SVM), model-naive Bayesian (Naive Bayesian Model, NBM), convolutional neural networks (Convolutional
Neural Network, CNN) etc. classifiers (Classifier) training obtain.In addition, critical point detection model is also possible to
As made of certain classification functions (such as softmax function etc.) training in advance.
In some optional implementations of the present embodiment, critical point detection model is convolutional neural networks.
In these optional implementations, includes multiple convolutional layers in convolutional neural networks, there is very strong processing energy
Power.Whether convolutional neural networks not only can accurately classify finger tip as it can be seen that also accurately determining fingertip location information of selling.
In some optional implementations of the present embodiment, critical point detection model is obtained by following steps training:
Obtain training sample set, wherein training sample concentrate sample include the topography comprising gesture, and with it is each
The finger tip location information and finger tip visual information for the gesture that a topography is included；
It is using topography as input, the finger tip location information of the included gesture of the topography and finger tip is visible
Information obtains critical point detection model as output, training initial key point detection model.
In these optional implementations, initial key point detection model is up for trained critical point detection mould
Type.After being trained using sample, then critical point detection can be obtained to after critical point detection mode input topography
The finger tip location information and finger tip visual information of the included gesture of the topography of model output.Pass through a large amount of sample
It is trained, available accurate critical point detection model.
In some optional implementations of the present embodiment, key point information further include: for each hand of gesture
Finger tip indicates the whether visible finger tip visual information of the finger tip.
In these optional implementations, corresponding 5 finger tips of a gesture, some gestures may in the picture not
Have and whole finger tips is presented.Namely the different gestures finger tip number that is showed can be and be not quite similar.Pass through hand
Finger tip visual information can determine which finger tip is visible in the picture, which is sightless.Specifically, above-mentioned to hold
Row main body all determines whether the finger tip is visible for each finger tip of each gesture.
In this way, in the present embodiment, above-mentioned executing subject can determine the finger tip position of the included gesture of topography
Confidence breath and finger tip visual information.For example, there is a gesture in topography, five hands from thumb to little finger
The finger tip of finger number 1,2,3,4 and 5 respectively.It can be shown from critical point detection model with output test result, testing result
State in finger tip 1,5 as it can be seen that 2,3,4 it is invisible and 1,5 location information.
The key point of finger tip is the high important key point of applying frequency, which these implementations can accurately show
The key point of finger tip is as it can be seen which is invisible, and the subsequent visibility according to the finger tip of realization is to the specified finger tip in image
It is handled, to enhance the popularity of testing result application.It for example, can be in the picture to where thumb finger tip
Position add pattern, can be covering or superposition by the way of.
With continued reference to the schematic diagram that Fig. 3, Fig. 3 are according to the application scenarios of the image processing method of the present embodiment.?
In the application scenarios of Fig. 3, executing subject 301 can obtain the gesture of the image comprising gesture from local or other electronic equipments
Frame 302, wherein gesture frame is used to indicate the position of the included gesture of image.Executing subject 301 extracts hand from image
It include the topography 303 of gesture indicated by gesture frame.Topography 303 is inputted into critical point detection model, to determine figure
As the key point information 304 of the finger tip of included gesture, wherein key point information includes finger tip location information.
The method provided by the above embodiment of the application is able to detect the key point of important finger tip, avoids in gesture
The very low key point of applying frequency carries out detecting caused invalid detection, improves detection efficiency.
With further reference to Fig. 4, as the realization to method shown in above-mentioned each figure, this application provides a kind of image procossing dresses
The one embodiment set, the Installation practice is corresponding with embodiment of the method shown in Fig. 2, which specifically can be applied to respectively
In kind electronic equipment.
As shown in figure 4, the image processing apparatus 400 of the present embodiment includes: acquiring unit 401, extraction unit 402 and determines
Unit 403.Wherein, acquiring unit 401 are configured to obtain the gesture frame of the image comprising gesture, wherein gesture frame is used
In the position of instruction the included gesture of image；Extraction unit 402, is configured to from image, extracts indicated by gesture frame
Topography comprising gesture；Determination unit 403 is configured to topography inputting critical point detection model, to determine figure
As the key point information of the finger tip of included gesture, wherein key point information includes finger tip location information.
In some embodiments, the acquiring unit 401 of image processing apparatus 400 can be from local or other electronic equipments
Obtain the gesture frame of the image comprising gesture.Gesture frame can delimit out gesture, to indicate the position of gesture.Specifically,
Gesture frame can be rectangle.The location information of gesture frame storage frame can be used only in above-mentioned executing subject, in the picture
Gesture frame is not shown.In addition, gesture frame can also be intuitively presented in the picture.
In some embodiments, for extraction unit 402 from above-mentioned image, extracting includes gesture indicated by gesture frame
Topography.Specifically, above-mentioned executing subject can cut out the topography in gesture frame, gesture side from above-mentioned image
The sideline of frame and the sideline of topography coincide.
In some embodiments, the topography extracted can be inputted critical point detection model by determination unit 403, be obtained
To the key point information exported from critical point detection model.Key point information is the pass of the finger tip of the included gesture of above-mentioned image
Key point information.Key point information may include finger tip location information.Finger tip location information can be finger tip in Local map
As the coordinate in (or image).
In some optional implementations of the present embodiment, key point information further include: for each hand of gesture
Finger tip indicates the whether visible finger tip visual information of the finger tip.
In some optional implementations of the present embodiment, acquiring unit is further configured to: carrying out hand to image
Gesture detection, to determine the gesture frame of the position of instruction the included gesture of image.
In some optional implementations of the present embodiment, critical point detection model is convolutional neural networks.
In some optional implementations of the present embodiment, critical point detection model is obtained by following steps training:
Obtain training sample set, wherein training sample concentrate sample include the topography comprising gesture, and with each Local map
As the finger tip location information and finger tip visual information of the gesture for being included；Using topography as input, by the Local map
It is exported as the finger tip location information and finger tip visual information of included gesture are used as, training initial key point detection model,
Obtain critical point detection model.
Below with reference to Fig. 5, it illustrates the computer systems 500 for the electronic equipment for being suitable for being used to realize the embodiment of the present application
Structural schematic diagram.Electronic equipment shown in Fig. 5 is only an example, function to the embodiment of the present application and should not use model
Shroud carrys out any restrictions.
As shown in figure 5, computer system 500 includes central processing unit (CPU and/or GPU) 501, it can be according to depositing
Storage is loaded into random access storage device (RAM) 503 in the program in read-only memory (ROM) 502 or from storage section 508
Program and execute various movements appropriate and processing.In RAM 503, also it is stored with system 500 and operates required various journeys
Sequence and data.Central processing unit 501, ROM 502 and RAM 503 are connected with each other by bus 504.Input/output (I/O)
Interface 505 is also connected to bus 504.
I/O interface 505 is connected to lower component: the storage section 506 including hard disk etc.；And including such as LAN card, tune
The communications portion 507 of the network interface card of modulator-demodulator etc..Communications portion 507 executes mailing address via the network of such as internet
Reason.Driver 508 is also connected to I/O interface 505 as needed.Detachable media 509, such as disk, CD, magneto-optic disk, half
Conductor memory etc. is mounted on as needed on driver 508, in order to as needed from the computer program read thereon
It is mounted into storage section 506.
Particularly, in accordance with an embodiment of the present disclosure, it may be implemented as computer above with reference to the process of flow chart description
Software program.For example, embodiment of the disclosure includes a kind of computer program product comprising be carried on computer-readable medium
On computer program, which includes the program code for method shown in execution flow chart.In such reality
It applies in example, which can be downloaded and installed from network by communications portion 507, and/or from detachable media
509 are mounted.When the computer program is executed by central processing unit (CPU and/or GPU) 501, the present processes are executed
The above-mentioned function of middle restriction.It should be noted that the computer-readable medium of the application can be computer-readable signal media
Or computer readable storage medium either the two any combination.Computer readable storage medium for example can be with
System, device or the device of --- but being not limited to --- electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, or it is any more than
Combination.The more specific example of computer readable storage medium can include but is not limited to: have one or more conducting wires
Electrical connection, portable computer diskette, hard disk, random access storage device (RAM), read-only memory (ROM), erasable type are programmable
Read-only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light storage device, magnetic are deposited
Memory device or above-mentioned any appropriate combination.In this application, computer readable storage medium can be it is any include or
The tangible medium of program is stored, which can be commanded execution system, device or device use or in connection.
And in this application, computer-readable signal media may include in a base band or as carrier wave a part propagate data
Signal, wherein carrying computer-readable program code.The data-signal of this propagation can take various forms, including but
It is not limited to electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be calculating
Any computer-readable medium other than machine readable storage medium storing program for executing, the computer-readable medium can send, propagate or transmit
For by the use of instruction execution system, device or device or program in connection.It is wrapped on computer-readable medium
The program code contained can transmit with any suitable medium, including but not limited to: wireless, electric wire, optical cable, RF etc., or
Above-mentioned any appropriate combination.
Flow chart and block diagram in attached drawing are illustrated according to the system of the various embodiments of the application, method and computer journey
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part of one module, program segment or code of table, a part of the module, program segment or code include one or more use
The executable instruction of the logic function as defined in realizing.It should also be noted that in some implementations as replacements, being marked in box
The function of note can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are actually
It can be basically executed in parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it to infuse
Meaning, the combination of each box in block diagram and or flow chart and the box in block diagram and or flow chart can be with holding
The dedicated hardware based system of functions or operations as defined in row is realized, or can use specialized hardware and computer instruction
Combination realize.
Being described in unit involved in the embodiment of the present application can be realized by way of software, can also be by hard
The mode of part is realized.Described unit also can be set in the processor, for example, can be described as: a kind of processor packet
Include acquiring unit, extraction unit and determination unit.Wherein, the title of these units is not constituted under certain conditions to the unit
The restriction of itself, for example, acquiring unit is also described as " obtaining the unit of the gesture frame of the image comprising gesture ".
As on the other hand, present invention also provides a kind of computer-readable medium, which be can be
Included in device described in above-described embodiment；It is also possible to individualism, and without in the supplying device.Above-mentioned calculating
Machine readable medium carries one or more program, when said one or multiple programs are executed by the device, so that should
Device: the gesture frame of the image comprising gesture is obtained, wherein gesture frame is used to indicate the position of the included gesture of image；
From image, the topography indicated by gesture frame comprising gesture is extracted；Topography is inputted into critical point detection model,
To determine the key point information of the finger tip of the included gesture of image, wherein key point information includes finger tip location information.
Above description is only the preferred embodiment of the application and the explanation to institute's application technology principle.Those skilled in the art
Member is it should be appreciated that invention scope involved in the application, however it is not limited to technology made of the specific combination of above-mentioned technical characteristic
Scheme, while should also cover in the case where not departing from foregoing invention design, it is carried out by above-mentioned technical characteristic or its equivalent feature
Any combination and the other technical solutions formed.Such as features described above has similar function with (but being not limited to) disclosed herein
Can technical characteristic replaced mutually and the technical solution that is formed.
Claims (12)
1. a kind of image processing method, comprising:
Obtain the gesture frame of the image comprising gesture, wherein the gesture frame is used to indicate the position of the included gesture of image
It sets；
From described image, the topography indicated by the gesture frame comprising gesture is extracted；
The topography is inputted into critical point detection model, to determine the key point of the finger tip of the included gesture of described image
Information, wherein the key point information includes finger tip location information.
2. according to the method described in claim 1, wherein, the key point information further include: for each finger of gesture
Point indicates the whether visible finger tip visual information of the finger tip.
3. according to the method described in claim 1, wherein, the acquisition includes the gesture frame of the image of gesture, comprising:
Gestures detection is carried out to described image, to determine the gesture frame of the position of instruction the included gesture of described image.
4. according to the method described in claim 1, wherein, the critical point detection model is convolutional neural networks.
5. according to the method described in claim 2, wherein, the critical point detection model is obtained by following steps training:
Obtain training sample set, wherein the sample that the training sample is concentrated includes the topography comprising gesture, and with it is each
The finger tip location information and finger tip visual information for the gesture that a topography is included；
Using topography as input, by the finger tip location information and finger tip visual information of the included gesture of the topography
As output, training initial key point detection model obtains the critical point detection model.
6. a kind of image processing apparatus, comprising:
Acquiring unit is configured to obtain the gesture frame of the image comprising gesture, wherein the gesture frame is used to indicate figure
As the position of included gesture；
Extraction unit is configured to from described image, extracts the topography indicated by the gesture frame comprising gesture；
Determination unit is configured to the topography inputting critical point detection model, to determine the included hand of described image
The key point information of the finger tip of gesture, wherein the key point information includes finger tip location information.
7. device according to claim 6, wherein the key point information further include: for each finger of gesture
Point indicates the whether visible finger tip visual information of the finger tip.
8. device according to claim 6, wherein the acquiring unit is further configured to:
Gestures detection is carried out to described image, to determine the gesture frame of the position of instruction the included gesture of described image.
9. device according to claim 6, wherein the critical point detection model is convolutional neural networks.
10. device according to claim 7, wherein the critical point detection model is obtained by following steps training:
Obtain training sample set, wherein the sample that the training sample is concentrated includes the topography comprising gesture, and with it is each
The finger tip location information and finger tip visual information for the gesture that a topography is included；
Using topography as input, by the finger tip location information and finger tip visual information of the included gesture of the topography
As output, training initial key point detection model obtains the critical point detection model.
11. a kind of electronic equipment, comprising:
One or more processors；
Storage device, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processors are real
Now such as method as claimed in any one of claims 1 to 5.
12. a kind of computer readable storage medium, is stored thereon with computer program, wherein when the program is executed by processor
Realize such as method as claimed in any one of claims 1 to 5.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810888877.7A CN109063653A (en) | 2018-08-07 | 2018-08-07 | Image processing method and device |
| PCT/CN2018/116340 WO2020029466A1 (en) | 2018-08-07 | 2018-11-20 | Image processing method and apparatus |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810888877.7A CN109063653A (en) | 2018-08-07 | 2018-08-07 | Image processing method and device |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110070063A (en) * | 2019-04-29 | 2019-07-30 | 北京字节跳动网络技术有限公司 | Action identification method, device and the electronic equipment of target object |
| CN110287891A (en) * | 2019-06-26 | 2019-09-27 | 北京字节跳动网络技术有限公司 | Gestural control method, device and electronic equipment based on human body key point |
| CN111382624A (en) * | 2018-12-28 | 2020-07-07 | 杭州海康威视数字技术股份有限公司 | Action recognition method, device, equipment and readable storage medium |
| CN111460858A (en) * | 2019-01-21 | 2020-07-28 | 杭州易现先进科技有限公司 | Method and device for determining pointed point in image, storage medium and electronic equipment |
| CN112262393A (en) * | 2019-12-23 | 2021-01-22 | 商汤国际私人有限公司 | Gesture recognition method and device, electronic equipment and storage medium |
| CN113792651A (en) * | 2021-09-13 | 2021-12-14 | 广州广电运通金融电子股份有限公司 | Gesture interaction method, device and medium integrating gesture recognition and fingertip positioning |
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| CN112183388A (en) * | 2020-09-30 | 2021-01-05 | 北京字节跳动网络技术有限公司 | Image processing method, apparatus, device and medium |
| CN112949437A (en) * | 2021-02-21 | 2021-06-11 | 深圳市优必选科技股份有限公司 | Gesture recognition method, gesture recognition device and intelligent equipment |
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| CN111382624A (en) * | 2018-12-28 | 2020-07-07 | 杭州海康威视数字技术股份有限公司 | Action recognition method, device, equipment and readable storage medium |
| CN111382624B (en) * | 2018-12-28 | 2023-08-11 | 杭州海康威视数字技术股份有限公司 | Action recognition method, device, equipment and readable storage medium |
| CN111460858A (en) * | 2019-01-21 | 2020-07-28 | 杭州易现先进科技有限公司 | Method and device for determining pointed point in image, storage medium and electronic equipment |
| CN111460858B (en) * | 2019-01-21 | 2024-04-12 | 杭州易现先进科技有限公司 | Method and device for determining finger tip point in image, storage medium and electronic equipment |
| CN110070063A (en) * | 2019-04-29 | 2019-07-30 | 北京字节跳动网络技术有限公司 | Action identification method, device and the electronic equipment of target object |
| WO2020220809A1 (en) * | 2019-04-29 | 2020-11-05 | 北京字节跳动网络技术有限公司 | Action recognition method and device for target object, and electronic apparatus |
| GB2598015A (en) * | 2019-04-29 | 2022-02-16 | Beijing Bytedance Network Tech Co Ltd | Action recognition method and device for target object, and electronic apparatus |
| GB2598015B (en) * | 2019-04-29 | 2023-07-05 | Beijing Bytedance Network Tech Co Ltd | Action recognition method and device for target object, and electronic apparatus |
| CN110287891A (en) * | 2019-06-26 | 2019-09-27 | 北京字节跳动网络技术有限公司 | Gestural control method, device and electronic equipment based on human body key point |
| CN112262393A (en) * | 2019-12-23 | 2021-01-22 | 商汤国际私人有限公司 | Gesture recognition method and device, electronic equipment and storage medium |
| CN113792651A (en) * | 2021-09-13 | 2021-12-14 | 广州广电运通金融电子股份有限公司 | Gesture interaction method, device and medium integrating gesture recognition and fingertip positioning |
| CN113792651B (en) * | 2021-09-13 | 2024-04-05 | 广州广电运通金融电子股份有限公司 | Gesture interaction method, device and medium integrating gesture recognition and fingertip positioning |
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