WO2016085122A1 - Appareil de correction de reconnaissance de geste d'après un motif utilisateur, et procédé associé - Google Patents

Appareil de correction de reconnaissance de geste d'après un motif utilisateur, et procédé associé Download PDF

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Publication number
WO2016085122A1
WO2016085122A1 PCT/KR2015/011022 KR2015011022W WO2016085122A1 WO 2016085122 A1 WO2016085122 A1 WO 2016085122A1 KR 2015011022 W KR2015011022 W KR 2015011022W WO 2016085122 A1 WO2016085122 A1 WO 2016085122A1
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WIPO (PCT)
Prior art keywords
motion
recognized
value
probability
sensor
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PCT/KR2015/011022
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English (en)
Korean (ko)
Inventor
성연식
김필영
김지원
손준혁
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계명대학교 산학협력단
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Publication of WO2016085122A1 publication Critical patent/WO2016085122A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • G06T7/344—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/35—Determination of transform parameters for the alignment of images, i.e. image registration using statistical methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/38—Registration of image sequences
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras

Definitions

  • the present invention relates to an apparatus and method for correcting motion recognition based on a user pattern, and more particularly, a motion recognition correction device capable of increasing a recognition rate of a physical motion by determining and correcting an error occurring when a body motion is recognized through a sensor. And to a method thereof.
  • a human-machine interface is generally achieved through a keyboard, a remote controller, a mouse, or a keypad provided in an electronic device, but as a function of an information communication device is developed, a finger or a pen is brought into contact with a touch panel using a touch sensor.
  • Touch-based systems are being utilized. However, this has a problem in that the touch is not properly made when a glove, water, or dust is on the hands, and recently, convenience functions that can adjust the device with only a minute motion of the user have been developed to maximize user convenience. .
  • the motion-based interface that allows the user to interact naturally with the computer through the user's intuitive movement, such as the user's body movements and gestures are attracting attention.
  • various expressions are possible and convenient to use, and there are various applications such as game / entertainment, medical / special treatment, and education.
  • the motion-based interface has a problem in that it is expensive and body motion recognition is not smooth. Recently, the number of sensors tends to be minimized in order to lower the product price of the body motion recognition device. As a result, the recognition rate of the body motion is lowered and the error occurrence rate is increased. In particular, since the sensor in the device is located only underneath, the parts covered by other objects or other body parts have a high recognition error rate, which makes it difficult for a user to perform a desired operation and causes inconveniences.
  • An object of the present invention is to provide a motion recognition correction apparatus and method for determining the recognition and correction of errors occurring when the body motion is recognized through the sensor to increase the recognition rate of the body motion.
  • a motion pattern storage unit for storing the numerical values of the various body movements and the probability values thereof, the sensor sensed by the sensor
  • a motion recognition unit for recognizing at least one body motion of the user using data
  • a numerical conversion unit for converting a body motion recognized by the sensor into a numerical value
  • comparing a probability value of the numerical value with a first reference value If the value is less than the first reference value, the error determining unit determines the physical motion recognized by the sensor as an error, and if the determination result is the error, the physical motion having the highest probability value among the stored motion patterns.
  • a motion correction unit for correcting and recognizing.
  • the motion pattern storage unit may store probability values calculated by applying a Bayesian algorithm to numerical values of successive physical motions as in the following equation.
  • the value is the numerical value of the body as recognized at time t-1,
  • the value represents the numerical value of the body recognized at t-2 hours.
  • the numerical conversion unit may digitize and store at least one of the recognized arm direction, leg direction, head direction and the degree of arm bending, the degree of leg bending, and the degree of trunk bending of the body.
  • the numerical conversion unit may obtain a numerical value of the recognized physical motion as follows.
  • h t represents the numerical value of the body as recognized at time t
  • y is a variable used to increase the probability.
  • the physical motion recognized by the sensor may be determined as the physical motion of the user.
  • the motion correction unit converts the body motion recognized by the sensor into a body motion having the highest probability value, and recognizes the highest motion value. If the probability value is less than or equal to the second reference value, the probability value may be recognized as a body motion recognized by the sensor.
  • the motion corrector may generate a control signal corresponding to the recognized body motion and transmit the generated control signal to the paired electronic device.
  • a motion recognition correction method using a user pattern based motion recognition correction device comprising: storing numerical values and probability values of various body motions, at least one physical motion of a user using data sensed by the sensor Recognizing a body, converting a body motion recognized by the sensor into a numerical value, comparing a probability value for the numerical value with a first reference value, and if the body is smaller than the first reference value, Determining an operation as an error, and correcting and recognizing a motion as a body motion having the highest probability value among the stored operation patterns when it is determined as an error.
  • the motion recognition correction apparatus determines an error on a motion recognized by a sensor based on a motion pattern of a user, recognizes a motion with a high probability value among existing motion patterns, and lowers a motion error rate. To perform the desired operation.
  • FIG. 1 is a block diagram illustrating a motion recognition correction device according to an embodiment of the present invention.
  • FIG. 2 is a block diagram of a motion recognition correction device according to an embodiment of the present invention.
  • FIG. 3 is a flowchart illustrating a method of correcting hand gestures in a body motion according to an exemplary embodiment of the present invention.
  • FIG. 4 is an exemplary diagram for describing a sequential hand gesture in a recognized body motion according to an embodiment of the present invention.
  • FIG. 1 is a block diagram illustrating a motion recognition correction device according to an embodiment of the present invention.
  • the gesture recognition correcting apparatus 200 may be implemented by being connected to the gesture recognition sensor 100 and the electronic device 300.
  • the motion recognition sensor 100 recognizes a movement and a position of a measurement target.
  • the motion recognition sensor 100 may use a geomagnetism, an acceleration, a gyro sensor, or the like. Movement may be recognized by wearing gloves using an acceleration sensor, a gyro sensor, and a bending sensor, or a sensor may be worn on a camera system, an infrared reflector, a wrist, or a body, and a band equipped with a sensor may be recognized.
  • the existing camera mounted on the electronic device so that it can be driven only by software without additional hardware, it recognizes the motion or installs two or more cameras and measures the distance using the difference between the images viewed from each camera. Can be recognized.
  • the motion may be recognized using Kinect structure, time of flight (ToF), Point Cloud, Body Gesture, etc., but is not limited thereto.
  • the motion recognition correction apparatus 200 may be manufactured and used as a USB memory and mounted between the motion recognition sensor and the electronic device, and may be separated from the electronic device so that the motion recognition correction device 200 does not need to be separately installed and may be used in various ways.
  • the motion recognition correcting apparatus 200 recognizes a user's body motion using the information sensed by the motion recognition sensor 100 and determines a recognition error of the recognized body motion based on a Bayesian probability. If it is determined that the recognition error, the corrected body motion is corrected, and a control signal corresponding to the corrected body motion or correctly recognized body motion is transmitted to the paired electronic device 300 to control the operation of the electronic device 300. .
  • the electronic device 300 is a device controlled according to the motion detected by the motion recognition sensor 100, and may be a human-machine interface such as a digital TV, a set-top box, a cellular phone, a tablet, a PC monitor, and the like. The device is not limited and can be applied. In addition, the electronic device 300 may receive a control signal from the motion recognition correcting apparatus 200 according to the detected motion and perform a function according to the control signal.
  • FIG. 2 is a block diagram of a motion recognition correction device according to an embodiment of the present invention.
  • Motion recognition correction apparatus 200 is a motion pattern storage unit 210, motion recognition unit 220, numerical conversion unit 230, error determination unit 240, motion correction unit 250 It includes.
  • the operation pattern storage unit 210 stores numerical values and Bayesian probability values of various operation patterns of the user. Since the conditions vary according to the use environment such as the frequency and type of operation patterns used for each user, the operation pattern storage unit 210 learns and stores data (value values and Bayesian probability values) for the operation patterns suitable for each user. Can be.
  • the motion pattern storage unit 210 stores probability values through learning about not only one body motion but also continuous body motions. Can be.
  • the operation pattern storage 210 stores the probability calculated through the Bayesian algorithm as shown in Equation 1 below.
  • Represents the Bayesian probability of one recognized body Value is the numerical value of the body recognized at t-1 hours The value represents the numerical value of the body as recognized at t-2 hours.
  • the motion recognition unit 220 receives the data sensed by the motion recognition sensor 100 to recognize a user's body motion. For example, a hand gesture among the body motions of the user may be recognized by sensing data of various hand gestures such as two fingers or a fist. In addition, the motion recognition unit 220 may recognize a body motion that changes continuously.
  • the numerical conversion unit 230 converts at least one or more body motions recognized by the motion recognition unit 220 into numerical values.
  • the conversion of the numerical value of the body motion may be set in various ways according to the sensor that recognizes the motion and the electronic device using the motion.
  • the direction of the arm, leg, head, and the degree of bending of the body For example, the direction of the arm, leg, head, and the degree of bending of the body, the degree of bending of the leg, the degree of trunk bending, etc., or the direction of the hand, the direction of the palm, the position, the direction of the fingers, the degree of bending of the knuckles. It converts the numerical values according to the information received by the motion recognition sensor that is actually used for the position pointed by the finger and the like.
  • the error determination unit 240 obtains a probability value for the numerical value converted by the numerical conversion unit 230 from the operation pattern storage unit 210, and operates when the probability value for the numerical value is smaller than the first reference value.
  • the body motion recognized by the recognition unit 220 is determined to be an error.
  • the operation corrector 250 searches for the highest probability value among the operation patterns stored in the operation pattern storage 210, and the highest probability value is greater than the second reference value. If it is large, it is recognized by correcting the body motion with the highest probability value. In addition, the motion corrector 250 transmits a control signal corresponding to the body motion corrected or recognized as the first body motion to the electronic device 300.
  • the motion recognition correcting apparatus 200 does not necessarily recognize and correct only one body part. That is, the motion recognition correcting apparatus 200 may recognize and correct two or more different body parts together.
  • FIG. 3 is a flowchart illustrating a method for correcting a hand gesture in a body motion according to an exemplary embodiment of the present invention
  • FIG. 4 is an exemplary diagram for describing a continuous hand gesture in a recognized body motion according to an exemplary embodiment of the present invention.
  • a probability of performing three third operations is determined in advance through learning and Bayesian probabilities and stored in the operation pattern storage 210.
  • the probability is 50%. It is assumed that there is a 30% probability in the case of extending and stopping only (3-2 movement) and 20% in the case of fist-taking movement (3-3 movement).
  • the gesture recognition correcting apparatus 200 recognizes the third hand gesture through the data transmitted through the gesture recognition sensor 100 (S310). That is, the motion recognition unit 220 recognizes which motion the third hand gesture corresponds to by using the sensing information recognized by the motion recognition sensor.
  • the gesture recognition unit 220 recognizes the third gesture as a fist fist (3-3).
  • the motion recognition correcting apparatus 200 converts the recognized hand motion into a numerical value.
  • the numerical value is to digitize at least one of the direction of the palm, the shape of the finger, whether the left or right hand and the degree of bending.
  • 0 indicates the bottom
  • 1 indicates the upward direction
  • '-' indicates the left direction
  • '+' indicates the right direction.
  • the finger of the left hand or the right hand may be represented by 1 to 5.
  • the shape of the hand gesture can be numerically converted by combining a number or a sign indicating the direction of the palm, the shape of the finger, whether the left hand or the right hand is, and the degree of bending.
  • h t represents the numerical value of the hand recognized at time t
  • Is It is a floor function that is less than or equal to the largest integer
  • y is a variable used to increase the probability.
  • the direction of the hand is- From + Since the probability of the other direction having the same value is represented by a small value. So, to increase the probability, divide and multiply the predefined variable y.
  • h t is measured as "1,2,3,4,5,6,7,8,9,10"
  • y is 3
  • h ' t Converts to 0,0,3,3,3,6,6,6,9,9. Since 1 and 2 are the same group, 3, 4, and 5 can be grouped into the same group, so the probability of getting the same number can be increased. In other words, y also binds the value of h ' t in a certain unit.
  • the motion recognition correcting apparatus 200 compares whether the probability value of the numerical value is smaller than the first reference value (S330).
  • the first reference value is a lower limit threshold and is a variable that can be changed in design according to the recognition accuracy required by the user.
  • the motion recognition correcting apparatus 200 determines the third motion recognized by the motion recognition sensor 100 as a correctly recognized hand motion. In operation S340, the third operation may be performed without further correction.
  • the error determination unit 240 because the probability value of the recognized 3-3 motion (fisting motion) is 20% greater than the first reference value. Determines a hand gesture (operation 3-3) recognized by the gesture recognition unit 220 as a third operation.
  • the motion recognition correcting apparatus 200 determines that the recognized third operation is incorrectly recognized.
  • the gesture recognition process may be performed. It is determined that an error has occurred.
  • the motion recognition correcting apparatus 200 is recognized.
  • the third operation (operation 3-3) is determined to be a recognition error.
  • step S340 If it is determined in step S340 that the recognition error, the motion recognition correction apparatus 200 after searching for the hand gesture having the highest probability value among the pre-stored operation patterns for the third operation after the first operation, the second operation, and then It is compared whether the high probability value is greater than the second reference value (S350).
  • the second reference value is an upper limit and is a variable that can be changed in design according to the recognition accuracy required by the user, similarly to the first reference value. In addition, it is obvious that the second reference value is higher than the first reference value.
  • the gesture recognition correcting apparatus 200 may perform the hand gesture first recognized by the gesture recognition sensor 100 in operation S350 (operation 3-3). ) Is determined as the third operation in step S310.
  • the probability value of the hand gesture having the highest probability value is the same as or smaller than the second reference value, it is determined that the reliability to be recognized as the hand gesture having the highest probability value is low.
  • the motion recognized by the motion recognition sensor 100 is determined as a third motion.
  • the motion recognition correcting apparatus determines a hand motion (operation 3-3) recognized by the motion recognition sensor 100 as the third operation again as in S350.
  • the motion recognition correcting apparatus 200 recognizes the hand gesture having the highest probability value as the third operation (S360).
  • the probability value of the hand gesture having the highest probability value is larger than the second reference value, it is determined that the hand gesture having the highest probability value is high in recognition of the third gesture, The hand gesture having the highest probability value is determined as the third gesture.
  • the motion recognition correcting apparatus 200 may use the motion recognition sensor ( The 3-1 operation is determined as the third operation without being recognized as the hand gesture recognized by the operation 100).
  • the motion recognition correcting apparatus 200 determines whether or not the motion of the hand gesture recognized by the motion recognition sensor 100 is substantially corrected through the first reference value and the second reference value. Will be decided.
  • the first reference value will be set high and the second reference value will be set low. have.
  • the motion recognition correcting apparatus 200 generates a control signal corresponding to the finally recognized motion and transmits it to the electronic device 300. That is, the motion recognition correcting apparatus 200 transmits a control signal corresponding to the motion first recognized by the motion recognition sensor 100 or the motion corrected by the hand motion having the highest probability value to the electronic device 300.
  • control signal corresponding to the combination of the first operation-second operation-finally recognized third operation is transmitted to the electronic device 300.
  • the motion recognition correcting apparatus determines whether an error is detected in the motion recognized by the sensor based on the motion pattern of the user, and recognizes the motion as having a high probability value among the existing motion patterns. It lowers the operation error rate and allows the user to perform the desired operation.

Abstract

L'invention concerne un appareil de correction de reconnaissance de geste d'après un motif utilisateur, ainsi qu'un procédé associé. L'invention concerne un procédé de correction de reconnaissance de geste utilisant un appareil de correction de reconnaissance de geste d'après un motif utilisateur, ledit procédé consistant à : stocker les valeurs numériques de différents gestes corporels et les valeurs de probabilité pour les valeurs numériques ; reconnaître au moins un geste corporel d'un utilisateur au moyen des données détectées par un capteur ; convertir le geste corporel reconnu par le capteur en une valeur numérique ; comparer une valeur de probabilité pour la valeur numérique avec une première valeur de référence et, si la valeur de probabilité est inférieure à la première valeur de référence, déterminer que le geste corporel reconnu par le capteur est une erreur ; et s'il est déterminé que le geste corporel est une erreur, reconnaître le geste corporel reconnu en corrigeant le geste corporel en un motif de geste ayant la valeur de probabilité la plus élevée parmi les motifs de geste stockés.
PCT/KR2015/011022 2014-11-28 2015-10-19 Appareil de correction de reconnaissance de geste d'après un motif utilisateur, et procédé associé WO2016085122A1 (fr)

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KR1020140168494A KR101596600B1 (ko) 2014-11-28 2014-11-28 사용자 패턴 기반의 동작 인식 보정 장치 및 그 방법
KR10-2014-0168494 2014-11-28

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018232557A1 (fr) * 2017-06-19 2018-12-27 深圳市酷浪云计算有限公司 Procédé et appareil de reconnaissance de mouvements d'exercice, et dispositif électronique

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102171459B1 (ko) * 2018-12-07 2020-10-29 테크빌교육 주식회사 사용자의 손동작을 인식하는 장치 및 이를 이용한 방법
KR102322968B1 (ko) * 2021-04-29 2021-11-09 주식회사 유토비즈 사용자의 손동작에 따른 명령 입력 장치 및 이를 이용한 명령 입력 방법

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20090124172A (ko) * 2008-05-29 2009-12-03 고려대학교 산학협력단 손의 모양 변화에 기초하여 제어되는 가상 마우스 장치 및그 구동 방법
KR20100041464A (ko) * 2008-10-14 2010-04-22 고려대학교 산학협력단 3차원 손 모델 생성 기술을 이용한 가상 입력 방법 및 장치
KR20130066812A (ko) * 2011-12-13 2013-06-21 (주) 미디어인터랙티브 동작 인식 방법, 장치 및 이 방법을 수행하는 컴퓨터 판독 가능한 기록 매체
KR20130141657A (ko) * 2010-12-29 2013-12-26 톰슨 라이센싱 제스처 인식을 위한 시스템 및 방법

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20090124172A (ko) * 2008-05-29 2009-12-03 고려대학교 산학협력단 손의 모양 변화에 기초하여 제어되는 가상 마우스 장치 및그 구동 방법
KR20100041464A (ko) * 2008-10-14 2010-04-22 고려대학교 산학협력단 3차원 손 모델 생성 기술을 이용한 가상 입력 방법 및 장치
KR20130141657A (ko) * 2010-12-29 2013-12-26 톰슨 라이센싱 제스처 인식을 위한 시스템 및 방법
KR20130066812A (ko) * 2011-12-13 2013-06-21 (주) 미디어인터랙티브 동작 인식 방법, 장치 및 이 방법을 수행하는 컴퓨터 판독 가능한 기록 매체

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
HUH, SUNG - JU ET AL.: "A Hierarchical Bayesian Network for Real-Time Continuous Hand Gesture Recognition", JOURNAL OF KIISE: SOFTWARE AND APPLICATIONS, vol. 36, no. 12, December 2009 (2009-12-01), pages 1028 - 1033 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018232557A1 (fr) * 2017-06-19 2018-12-27 深圳市酷浪云计算有限公司 Procédé et appareil de reconnaissance de mouvements d'exercice, et dispositif électronique

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