CN109634415A - It is a kind of for controlling the gesture identification control method of analog quantity - Google Patents
It is a kind of for controlling the gesture identification control method of analog quantity Download PDFInfo
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/017—Gesture based interaction, e.g. based on a set of recognized hand gestures
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- G06V40/113—Recognition of static hand signs
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- 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
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Abstract
The invention proposes a kind of for controlling the gesture identification control method of analog quantity, the hand 3d space data that the present invention utilizes depth camera LeapMotion to provide, according to the positional relationship and direction relations progress static gesture identification between joint each in single-frame images；The variation of joint data carries out dynamic hand gesture recognition in multiple image；And the continuous gesture identification based on continuous multiple frames image, in terms of continuous gesture identification, eliminating the shake as caused by manpower operating reason or LeapMotion data error leads to the interruption of continuous gesture, to realize the Analog control of continuous gesture.
Description
Technical field
The invention belongs to gesture identification control technology fields, more particularly to a kind of for controlling the gesture identification of analog quantity
Control method.
Background technique
Gesture has the characteristics that non-contact, manipulation is convenient as a kind of important way of human-computer interaction, and existing gesture is known
Other method mainly divides the method based on wearable device and vision.Method based on wearable device is to track hand using sensor
The motion profile and timing information of portion in space, advantage is can to obtain accurate hand data, the disadvantage is that needing to dress each
The complicated equipment of kind, influences the naturality of man-machine interaction experience.The method of view-based access control model is mainly based upon monocular cam and depth
Camera is spent, monocular cam is lost depth information when obtaining image, can only identify the gesture of particular category, such as static
Hand-type gesture；Be concentrated mainly in terms of depth camera static gesture identification, hand left and right brandish identification, control amount is also led
If some switching values, however need to carry out some equipment continuous Analog control in real life, such as equipment
Volume, temperature etc..
In addition, there is also some other problems when being controlled using gesture, it is on the one hand to exist between each gesture
Certain similitude；It on the other hand is during dynamic gesture, since everyone motor habit and finger movement are successive
Sequence is different, can usually generate the coupling of gesture, i.e., can include other dynamic gestures during some dynamic gesture.On
The solution for stating problem not only relies on specific recognition methods, it is also necessary to perform some processing in control logic, become to gesture
Some states during changing make the rejecting of corresponding constraint and interference gesture.
Summary of the invention
The invention aims to solve the problems of the prior art, a kind of gesture knowledge for controlling analog quantity is provided
Sex control method.
The present invention is achieved by the following technical solutions, and the present invention proposes a kind of for controlling the gesture identification of analog quantity
Control method,
Step 1 acquires hand 3d space data using camera, obtains a complete gesture motion；
The complete gesture motion is resolved into static gesture, dynamic gesture and continuous gesture by step 2；
Step 3, according between joint each in single-frame images positional relationship and direction relations carry out static gesture identification；
Step 4 is recognizing the laggard Mobile state gesture identification of static gesture or continuous gesture identification；If quiet recognizing
The laggard Mobile state gesture identification of state gesture thens follow the steps 5；If carrying out continuous gesture identification after recognizing static gesture
Execute step 7；
Step 5 carries out dynamic hand gesture recognition, if recognizing dynamic gesture thens follow the steps 6；If do not recognized dynamic
State gesture then executes the corresponding operation of static gesture；
Step 6 carries out continuous gesture identification, if recognizing continuous gesture, by the static gesture recognized, dynamic hand
Gesture and continuous gesture are merged, and gesture fusion results are obtained, and execute corresponding operation according to gesture fusion results；If no
It recognizes continuous gesture and then executes the corresponding operation of dynamic hand gesture recognition result；
If step 7 recognizes continuous gesture, corresponding operation is executed according to the recognition result of continuous gesture；If
Continuous gesture is not recognized, thens follow the steps 5.
Further, the static gesture includes digital 1 gesture, digital 2 gestures, digital 3 gestures and digital 5 gestures；Institute
Stating dynamic gesture includes that two finger kneading gestures, three finger kneading gestures, gesture of clenching fist and palm brandish gesture；The continuous gesture packet
Include horizontal plane linear motion gesture and horizontal plane circular motion gesture.
Further, digital 2 gesture is that thumb is stretched flat with index finger, remaining finger grips；Digital 3 gesture is thumb
Refer to, index finger and middle finger are stretched flat, remaining finger grips；Described two refer to that kneading gesture is from digital 2 gestures to finger tip contacts；Described three
Refer to that kneading gesture is from digital 3 gestures to finger tip contacts；The palm brandish gesture be the five fingers be stretched flat XOY plane it is suitable/inverse time
Needle is brandished；The horizontal plane linear motion gesture is that three finger kneadings are slided along the x axis；The horizontal plane circular motion gesture is
Two refer to that kneading is slided in XOZ plane around the fixed center of circle.
Further, the static gesture identification is based on the distance between each joint of hand and the centre of the palm ratio in single-frame images
Example and direction relations between each finger bone direction and centre of the palm direction vector distinguish.
Further, the dynamic hand gesture recognition is based on the variation of distance, hand between finger fingertip each in multiple image
Refer to that finger tip is identified to the change in location of the variation of the distance between the centre of the palm and the centre of the palm in space, the multiframe is discontinuous
Frame.
Further, the continuous gesture identification is to carry out gesture analysis based on continuous frame sequence.
Further, the hand joint data of adjacent 5 frame image are continuously acquired in continuous gesture identification, then by phase
Corresponding joint data weighting is averaged, finally using the average value collectively as the hand joint data of 5 frame images, to disappear
Except the shake of data.
The hand 3d space data that the present invention utilizes depth camera LeapMotion to provide, according to respectively being closed in single-frame images
Positional relationship and direction relations between section carry out static gesture identification；The variation of joint data carries out dynamic hand in multiple image
Gesture identification；And the continuous gesture identification based on continuous multiple frames image eliminates in terms of continuous gesture identification since manpower is grasped
Making shake caused by reason or LeapMotion data error leads to the interruption of continuous gesture, to realize the mould of continuous gesture
Analog quantity control.
Detailed description of the invention
Fig. 1 is of the present invention a kind of for controlling the gesture identification control method flow chart of analog quantity.
Specific embodiment
Technical solution in the embodiment of the present invention that following will be combined with the drawings in the embodiments of the present invention carries out clear, complete
Ground description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Based on this
Embodiment in invention, every other reality obtained by those of ordinary skill in the art without making creative efforts
Example is applied, shall fall within the protection scope of the present invention.
As shown in Figure 1, the present invention propose it is a kind of for controlling the gesture identification control method of analog quantity,
Step 1 acquires hand 3d space data using camera, obtains a complete gesture motion；
The complete gesture motion is resolved into static gesture, dynamic gesture and continuous gesture by step 2；
Step 3, according between joint each in single-frame images positional relationship and direction relations carry out static gesture identification；
Step 4 is recognizing the laggard Mobile state gesture identification of static gesture or continuous gesture identification；If quiet recognizing
The laggard Mobile state gesture identification of state gesture thens follow the steps 5；If carrying out continuous gesture identification after recognizing static gesture
Execute step 7；
Step 5 carries out dynamic hand gesture recognition, if recognizing dynamic gesture thens follow the steps 6；If do not recognized dynamic
State gesture then executes the corresponding operation of static gesture；
Step 6 carries out continuous gesture identification, if recognizing continuous gesture, by the static gesture recognized, dynamic hand
Gesture and continuous gesture are merged, and gesture fusion results are obtained, and execute corresponding operation according to gesture fusion results；If no
It recognizes continuous gesture and then executes the corresponding operation of dynamic hand gesture recognition result；
If step 7 recognizes continuous gesture, corresponding operation is executed according to the recognition result of continuous gesture；If
Continuous gesture is not recognized, thens follow the steps 5.
The static gesture includes digital 1 gesture, digital 2 gestures, digital 3 gestures and digital 5 gestures；The dynamic gesture
Gesture is brandished including two finger kneading gestures, three finger kneading gestures, gesture of clenching fist and palm；The continuous gesture includes that horizontal plane is straight
Line motion gesture and horizontal plane circular motion gesture.
Digital 1 gesture is stretched flat for index finger, remaining finger grips；Digital 2 gesture is that thumb is stretched flat with index finger,
Remaining finger grips；Digital 3 gesture is that thumb, index finger and middle finger are stretched flat, remaining finger grips；Digital 5 gesture is five
Finger is stretched flat；Described two refer to that kneading gesture is from digital 2 gestures to finger tip contacts；Described three refer to that kneading gesture is from digital 3 gestures
To finger tip contacts；The gesture of clenching fist is to hold with a firm grip from digital 5 gestures to the five fingers；It is that the five fingers are stretched flat that the palm, which brandishes gesture,
XOY plane is suitable/it brandishes counterclockwise；The horizontal plane linear motion gesture is that three finger kneadings are slided along the x axis；The horizontal plane
Circular motion gesture is that two fingers are mediated in XOZ plane around fixed center of circle sliding.
Static gesture identification is based on the distance between each joint of hand and the centre of the palm ratio in single-frame images and respectively
Direction relations between finger bone direction and centre of the palm direction vector (being pierced by perpendicular to palm plane and from the centre of the palm) carry out area
Point.Such as digital 1 gesture, the distance between index finger tip to the centre of the palm to be significantly greater than other finger fingertips between the centre of the palm away from
From while index finger most end bone (bone close to the centre of the palm) direction is perpendicular to centre of the palm vector, and the direction of other fingers bones
It is almost parallel with centre of the palm direction vector, the identification of digital 1 gesture can be completed based on above-mentioned two condition.
The dynamic hand gesture recognition is based on the variation of distance, finger fingertip to the palm between finger fingertip each in multiple image
The change in location of the variation of the distance between heart and the centre of the palm in space is identified that the multiframe is discontinuous frame.It is specific right
It should be related to as shown in table 1.
1 dynamic gesture explanation of table
Described two refer to the identification process of kneading gesture are as follows: obtain present frame swivel of hand data, calculate thumb tip and index finger
The distance between finger tip assert that the gesture is two finger kneading gestures, such as if the distance is less than a certain threshold value (preferably 30)
Fruit is greater than a certain threshold value and then reacquires data or terminate process.
It is described three refer to kneading gesture identification process are as follows: obtain present frame swivel of hand data, calculate present frame under thumb with
Index finger tip distance and thumb and middle fingertip distance, and calculate distance it is cumulative and；Obtain the 30th frame swivel of hand data in the past, meter
Calculate thumb and index finger tip distance and thumb and middle fingertip distance under the 30th frame in the past, and calculate distance it is cumulative and；Judgement refers to
The relative value and absolute value of sharp distance change, if relative value is that the distance of the 30th frame in the past adds up and subtract the distance of present frame
It is cumulative to be greater than a certain threshold value (preferably 0.5) divided by the value of the cumulative sum of the distance of the 30th frame of past with after, also, absolute value was
The distance for going the distance of the 30th frame to add up and subtract present frame adds up and is greater than a certain threshold value (preferably 60), and count is incremented, works as meter
When number is greater than 10, determine that the gesture is that three fingers are mediated.
The identification process of the gesture of clenching fist are as follows: obtain present frame swivel of hand data, calculate separately the five fingers finger tip to the centre of the palm
The distance between, calculate distance it is cumulative and；The 30th frame swivel of hand data in the past are obtained, calculate separately the five fingers finger tip between the centre of the palm
Distance, calculate distance it is cumulative and；Judge distance change percentage, i.e., the distance of the 30th frame is cumulative in the past and subtracts present frame
Distance is cumulative to be greater than a certain numerical value (preferably 0.4) divided by the value of the cumulative sum of the distance of the 30th frame of past with after, then it is assumed that the hand
Gesture is gesture of clenching fist, and data are reacquired if being not more than or terminate process.
The identification process for brandishing gesture are as follows: obtain the 30th frame centre of the palm coordinate (x30, y30, z30) in the past, obtained
Remove the 15th frame centre of the palm coordinate (x15, y15, z15), obtain present frame centre of the palm coordinate (x, y, z), calculate the 30th frame in the past and
The 15th frame centre of the palm XOY plane direction of motion vector (x15-x30, y15-y30) of past calculates the 15th frame and present frame hand in the past
Centre of the palm XOY plane direction of motion vector (x-x15, y-y15) calculates Outer Product of Vectors T, horizontal distance D and vertical range H, T=
(x15-x30) * (y-y15)-(y15-y30) * (x-x15), D=│ x-x30 │, H=│ y-y30 │, if D is big greater than 80 and H
Judge that T is equal to 0, is also less than 0 greater than 0 in 80, otherwise terminate process, terminates process if T is equal to 0, if T is greater than
0, then it represents that gesture is brandished counterclockwise, if T is less than 0, then it represents that brandish gesture clockwise.
The continuous gesture identification is to carry out gesture analysis based on continuous frame sequence.It is most main in continuous gesture identification
What is wanted is exactly some stability, that is, the gesture motion identified cannot generate shake, while corresponding control strip will be with the movement of hand
Consistent (following effect).Therefore in the Key dithering that the first step of identification is exactly data, the present invention is main in terms of Key dithering
Shake is eliminated using the mean value smoothing method of continuous 5 frame image and improves stability, that is, continuously acquires the hand of adjacent 5 frame image
Portion joint data, then corresponding joint data weighting is averaged, finally using the average value collectively as 5 frame images
Hand joint data.
The continuous gesture includes linear horizontal plane linear motion and nonlinear horizontal plane circular motion.
It is described linear motion gesture recognition methods specifically: obtain 1-5 frame hand data, obtain thumb, index finger and
Middle fingertip coordinate mean value smoothing x_T, x_I and x_M；6-10 frame hand data are obtained, thumb, index finger and middle fingertip are obtained
Coordinate mean value smoothing x1_T, x1_I and x1_M；Thumb, index finger and middle fingertip displacement are calculated according to the result that above-mentioned steps obtain
Accumulated value SumDis_T+=x_T-x1_T, SumDis_I+=x_I-x1_I and SumDis_M+=x_M-x1_M；Three are displaced
Accumulated value carries out range estimation, if meeting SumDis_T > 5, SumDis_I > 5 and SumDis_M > 5, gesture simultaneously
To be slided to X-axis positive direction；If meeting SumDis_T < -5, SumDis_I < -5 and < -5 SumDis_M, hand simultaneously
Gesture is to slide to X-axis negative direction.
The recognition methods of the circular motion gesture specifically: judge whether to initialize the center of circle, if it is obtain continuous 5
Frame centre of the palm coordinate, and centre of the palm coordinate is smoothed to a bit, radius and initial angle are set, so that it is determined that central coordinate of circle (ox, oy,
oz)；Continuous 5 frame centre of the palm coordinate is obtained if not initializing the center of circle, and centre of the palm coordinate is smoothed to a bit (x, y, z)；According to
Central coordinate of circle and the centre of the palm coordinate for being smoothed to any calculate direction vector (x-ox, z-oz)；It calculates between direction vector and-Z axis
Included angle A lpha；Compare x and ox coordinate size, if x > ox, exports circumferential angle Alpha, if x < ox, exports circle
All angle 360-Alpha.
Classification describes the recognition methods of various gestures in gesture identification, however a complete gesture control side
Method can generate interference and misrecognition, especially in difference due to the similitude and coupling between gesture comprising various gestures
When switching between gesture, misrecognition probability be will increase.That singly deposits can not settle the matter once and for all from recognition methods, therefore
It needs to use restraint in control logic, the switching of especially different gesture states.By a complete gesture point in the present invention
Solution is pocessed at the gesture of multiple and different types, and level is straight after reducing interfering with each other between gesture, such as two finger kneadings
Line moves this gesture, has been split into static 2 gesture of number, dynamic two finger kneading gesture and horizontal plane straight line
Motion gesture three parts, first with static gesture recognition detection to digital 2 gestures in control logic, after completing identification
State under connecing just only there are two types of, refer to one is two and mediate, another kind is that (including hand is from LeapMotion without any movement
Disappear in visual field), if detecting two finger kneading gestures on this basis, next state again only there are two types of, one is water
Flat line motion gesture, another kind are without any movement (including hand disappears from LeapMotion visual field).So being based on
Above-mentioned control logic all becomes the branch of only two states comprising the complicated gesture state of many branches, by this
Control logic solves interfering with each other between gesture, improves the stability of gestural control system.
Now by taking multimedia player as an example, illustrate specific implementation step of the invention, player corresponding to corresponding gesture
Function is as shown in table 2.
2 player function of table and corresponding gesture
Step a: judging player status, if closed state executes step b, if open state executes step c；
Step b: 1 gesture of static number opens player, executes step c；
Step c: determining whether to the operation (i.e. change player status) that corresponding function is done to player, if you do not need to
The operation of corresponding function executes step d, if necessary to corresponding feature operation, executes step e；
Step d: player current state is maintained, and executes step c；
Step e: static gesture identification is carried out, and executes step f；
Step f: whether there are execution linear motion gesture instruction or circular motion gesture instruction, if executing step without instruction
Rapid g executes step h, if there is instructing and being circular motion instruction, executes step if there is instructing and instructing for linear motion
i；
Step g: carrying out dynamic hand gesture recognition, brandish gesture if detected, step j is executed, if detecting hand of clenching fist
Gesture executes k, if detecting two finger kneading gestures, issues circular gesture and instructs and execute step f, if detecting that three fingers are mediated
Gesture issues linear motion gesture instruction and executes step f, if dynamic gesture, which is not detected, executes step c；
Step h: player progress adjusts (linear motion gesture), executes step c；
Step i: volume adjustment (circular motion gesture) executes step c；
Step j: program switching executes step c；
Step k: closing player, executes step a；
Above to provided by the present invention a kind of for controlling the gesture identification control method of analog quantity, detailed Jie has been carried out
It continues, used herein a specific example illustrates the principle and implementation of the invention, and the explanation of above embodiments is only
It is to be used to help understand method and its core concept of the invention；At the same time, for those skilled in the art, according to this hair
Bright thought, there will be changes in the specific implementation manner and application range, in conclusion the content of the present specification should not manage
Solution is limitation of the present invention.
Claims (7)
1. a kind of for controlling the gesture identification control method of analog quantity, it is characterised in that:
Step 1 acquires hand 3d space data using camera, obtains a complete gesture motion；
The complete gesture motion is resolved into static gesture, dynamic gesture and continuous gesture by step 2；
Step 3, according between joint each in single-frame images positional relationship and direction relations carry out static gesture identification；
Step 4 is recognizing the laggard Mobile state gesture identification of static gesture or continuous gesture identification；If recognizing static hand
The laggard Mobile state gesture identification of gesture thens follow the steps 5；It is executed if carrying out continuous gesture identification after recognizing static gesture
Step 7；
Step 5 carries out dynamic hand gesture recognition, if recognizing dynamic gesture thens follow the steps 6；If not recognizing dynamic hand
Gesture then executes the corresponding operation of static gesture；
Step 6, carry out continuous gesture identification, if recognizing continuous gesture, by the static gesture recognized, dynamic gesture and
Continuous gesture is merged, and gesture fusion results are obtained, and executes corresponding operation according to gesture fusion results；If do not identified
The corresponding operation of dynamic hand gesture recognition result is then executed to continuous gesture；
If step 7 recognizes continuous gesture, corresponding operation is executed according to the recognition result of continuous gesture；If no
Continuous gesture is recognized, thens follow the steps 5.
2. according to the method described in claim 1, it is characterized by: the static gesture include digital 1 gesture, digital 2 gestures,
Digital 3 gestures and digital 5 gestures；The dynamic gesture includes two finger kneading gestures, three finger kneading gestures, gesture of clenching fist and palm
Brandish gesture；The continuous gesture includes horizontal plane linear motion gesture and horizontal plane circular motion gesture.
3. according to the method described in claim 2, it is characterized by: digital 2 gesture be thumb be stretched flat with index finger, remaining hand
Finger gripping is tight；Digital 3 gesture is that thumb, index finger and middle finger are stretched flat, remaining finger grips；Described two refer to that kneading gesture is from number
2 gesture of word is to finger tip contacts；Described three refer to that kneading gesture is from digital 3 gestures to finger tip contacts；The palm brandishes gesture
The five fingers be stretched flat XOY plane it is suitable/brandish counterclockwise；The horizontal plane linear motion gesture is that three finger kneadings are slided along the x axis；
The horizontal plane circular motion gesture is that two fingers are mediated in XOZ plane around fixed center of circle sliding.
4. according to the method described in claim 3, it is characterized by: static gesture identification is based on hand in single-frame images
Direction relations between the distance between each joint and the centre of the palm ratio and each finger bone direction and centre of the palm direction vector come into
Row is distinguished.
5. according to the method described in claim 4, it is characterized by: the dynamic hand gesture recognition is based on each hand in multiple image
Refer between finger tip the distance between the variation of distance, finger fingertip to the centre of the palm change in location of variation and the centre of the palm in space into
Row identification, the multiframe are discontinuous frame.
6. according to the method described in claim 5, it is characterized by: the continuous gesture identification be based on continuous frame sequence come
Carry out gesture analysis.
7. according to the method described in claim 6, it is characterized by: continuously acquiring adjacent 5 frame in continuous gesture identification
The hand joint data of image, then corresponding joint data weighting is averaged, finally using the average value collectively as 5
The hand joint data of frame image, to eliminate the shake of data.
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|---|---|---|---|---|
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Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080005703A1 (en) * | 2006-06-28 | 2008-01-03 | Nokia Corporation | Apparatus, Methods and computer program products providing finger-based and hand-based gesture commands for portable electronic device applications |
| CN102609093A (en) * | 2012-02-16 | 2012-07-25 | 中国农业大学 | Method and device for controlling video playing by using gestures |
| CN103455794A (en) * | 2013-08-23 | 2013-12-18 | 济南大学 | Dynamic gesture recognition method based on frame fusion technology |
| CN104571482A (en) * | 2013-10-22 | 2015-04-29 | 中国传媒大学 | Digital device control method based on somatosensory recognition |
| CN104750397A (en) * | 2015-04-09 | 2015-07-01 | 重庆邮电大学 | Somatosensory-based natural interaction method for virtual mine |
| US20150220153A1 (en) * | 2013-10-25 | 2015-08-06 | Lsi Corporation | Gesture recognition system with finite state machine control of cursor detector and dynamic gesture detector |
| CN105334960A (en) * | 2015-10-22 | 2016-02-17 | 四川膨旭科技有限公司 | Vehicle-mounted intelligent gesture recognition system |
| CN105930785A (en) * | 2016-04-15 | 2016-09-07 | 丁盛 | Intelligent concealed-type interaction system |
| CN106934333A (en) * | 2015-12-31 | 2017-07-07 | 芋头科技（杭州）有限公司 | A kind of gesture identification method and system |
| CN106990840A (en) * | 2017-03-27 | 2017-07-28 | 联想(北京)有限公司 | control method and control system |
| CN107272899A (en) * | 2017-06-21 | 2017-10-20 | 北京奇艺世纪科技有限公司 | A kind of VR exchange methods, device and electronic equipment based on dynamic gesture |
| CN107578023A (en) * | 2017-09-13 | 2018-01-12 | 华中师范大学 | Man-machine interaction gesture identification method, apparatus and system |
| CN107765855A (en) * | 2017-10-25 | 2018-03-06 | 电子科技大学 | A kind of method and system based on gesture identification control machine people motion |
| CN108537147A (en) * | 2018-03-22 | 2018-09-14 | 东华大学 | A kind of gesture identification method based on deep learning |
| CN108549489A (en) * | 2018-04-27 | 2018-09-18 | 哈尔滨拓博科技有限公司 | A kind of gestural control method and system based on hand form, posture, position and motion feature |
| CN108572734A (en) * | 2018-04-23 | 2018-09-25 | 哈尔滨拓博科技有限公司 | A kind of gestural control system based on infrared laser associated image |
| CN108629272A (en) * | 2018-03-16 | 2018-10-09 | 上海灵至科技有限公司 | A kind of embedded gestural control method and system based on monocular cam |
| CN108646910A (en) * | 2018-03-20 | 2018-10-12 | 重庆邮电大学 | A kind of Three-Dimensional Dynamic finger text input system and method based on depth image |
-
2018
- 2018-12-11 CN CN201811507799.8A patent/CN109634415B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080005703A1 (en) * | 2006-06-28 | 2008-01-03 | Nokia Corporation | Apparatus, Methods and computer program products providing finger-based and hand-based gesture commands for portable electronic device applications |
| CN102609093A (en) * | 2012-02-16 | 2012-07-25 | 中国农业大学 | Method and device for controlling video playing by using gestures |
| CN103455794A (en) * | 2013-08-23 | 2013-12-18 | 济南大学 | Dynamic gesture recognition method based on frame fusion technology |
| CN104571482A (en) * | 2013-10-22 | 2015-04-29 | 中国传媒大学 | Digital device control method based on somatosensory recognition |
| US20150220153A1 (en) * | 2013-10-25 | 2015-08-06 | Lsi Corporation | Gesture recognition system with finite state machine control of cursor detector and dynamic gesture detector |
| CN104750397A (en) * | 2015-04-09 | 2015-07-01 | 重庆邮电大学 | Somatosensory-based natural interaction method for virtual mine |
| CN105334960A (en) * | 2015-10-22 | 2016-02-17 | 四川膨旭科技有限公司 | Vehicle-mounted intelligent gesture recognition system |
| CN106934333A (en) * | 2015-12-31 | 2017-07-07 | 芋头科技（杭州）有限公司 | A kind of gesture identification method and system |
| CN105930785A (en) * | 2016-04-15 | 2016-09-07 | 丁盛 | Intelligent concealed-type interaction system |
| CN106990840A (en) * | 2017-03-27 | 2017-07-28 | 联想(北京)有限公司 | control method and control system |
| CN107272899A (en) * | 2017-06-21 | 2017-10-20 | 北京奇艺世纪科技有限公司 | A kind of VR exchange methods, device and electronic equipment based on dynamic gesture |
| CN107578023A (en) * | 2017-09-13 | 2018-01-12 | 华中师范大学 | Man-machine interaction gesture identification method, apparatus and system |
| CN107765855A (en) * | 2017-10-25 | 2018-03-06 | 电子科技大学 | A kind of method and system based on gesture identification control machine people motion |
| CN108629272A (en) * | 2018-03-16 | 2018-10-09 | 上海灵至科技有限公司 | A kind of embedded gestural control method and system based on monocular cam |
| CN108646910A (en) * | 2018-03-20 | 2018-10-12 | 重庆邮电大学 | A kind of Three-Dimensional Dynamic finger text input system and method based on depth image |
| CN108537147A (en) * | 2018-03-22 | 2018-09-14 | 东华大学 | A kind of gesture identification method based on deep learning |
| CN108572734A (en) * | 2018-04-23 | 2018-09-25 | 哈尔滨拓博科技有限公司 | A kind of gestural control system based on infrared laser associated image |
| CN108549489A (en) * | 2018-04-27 | 2018-09-18 | 哈尔滨拓博科技有限公司 | A kind of gestural control method and system based on hand form, posture, position and motion feature |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110109547A (en) * | 2019-05-05 | 2019-08-09 | 芋头科技（杭州）有限公司 | Order Activiation method and system based on gesture identification |
| WO2021218126A1 (en) * | 2020-04-26 | 2021-11-04 | 武汉Tcl集团工业研究院有限公司 | Gesture identification method, terminal device, and computer readable storage medium |
| CN112084898A (en) * | 2020-08-25 | 2020-12-15 | 西安理工大学 | Assembling operation action recognition method based on static and dynamic separation |
| CN112084898B (en) * | 2020-08-25 | 2024-02-09 | 西安理工大学 | Assembly operation action recognition method based on static and dynamic separation |
| CN112446291A (en) * | 2020-10-26 | 2021-03-05 | 杭州易现先进科技有限公司 | Gesture recognition method and device, electronic device and storage medium |
| CN112509668A (en) * | 2020-12-16 | 2021-03-16 | 成都翡铭科技有限公司 | Method for identifying whether hand is gripping or not |
| WO2022166243A1 (en) * | 2021-02-07 | 2022-08-11 | 青岛小鸟看看科技有限公司 | Method, apparatus and system for detecting and identifying pinching gesture |
| US11776322B2 (en) | 2021-02-07 | 2023-10-03 | Qingdao Pico Technology Co., Ltd. | Pinch gesture detection and recognition method, device and system |
| CN113126753A (en) * | 2021-03-05 | 2021-07-16 | 深圳点猫科技有限公司 | Implementation method, device and equipment for closing equipment based on gesture |
| CN113741701A (en) * | 2021-09-30 | 2021-12-03 | 之江实验室 | Brain nerve fiber bundle visualization method and system based on somatosensory gesture control |
| WO2023070933A1 (en) * | 2021-10-26 | 2023-05-04 | 深圳市鸿合创新信息技术有限责任公司 | Gesture recognition method and apparatus, device, and medium |
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