KR20160138729A - Feature extraction method for motion recognition in image and motion recognition method using skeleton information - Google Patents

Abstract

A user motion recognizing method using skeleton information, by a motion recognizing apparatus, includes the steps of: performing a neural network learning by using a first feature point based on a plurality of first joint points among human joint points in a training image including a plurality of human training-movements; obtaining an input image including a human input-movement by using a camera; extracting a second feature point based on a plurality of second joint points, which have positions same as the first joint points, among the human joint points in the input image; and inputting the second feature point into a neural network formed by the neural network learning to determine a training-movement corresponding to the input movement.

Description

TECHNICAL FIELD [0001] The present invention relates to a feature extraction method for recognizing motion in an image, and a user motion recognition method using skeleton information. [0002]

The techniques described below relate to techniques for recognizing user actions on a video.

Motion recognition technology is a technology that recognizes the user's body movements and interacts with the computer. Human motion can be used as an interfacing input to a computer device.

Typical examples of motion recognition are smart phones based on gyro sensors, game consoles based on controller sensors, and smart phones based on camera sensors.

U.S. Published Patent Application No. US2010-0199228 U.S. Published Patent Application No. US2012-0157198

The Kinect of the MS extracts the skeleton information of the person included in the image and recognizes the motion. However, since Kinect tracks human skeleton information at a long distance, there is a limit in not providing accurate recognition.

The technique described below is to provide an operation recognition method using a feature point that can accurately recognize an operation among skeleton information of a person included in an image.

A method for extracting a feature point for motion recognition in an image includes the steps of acquiring an image having depth information by a motion recognition device, extracting an object included in the motion recognition device, extracting skeleton information of the object, Determining the coordinates of the plurality of points in the skeleton information by the apparatus; and determining, as the feature points, the angle formed by the plurality of points and the angle formed by the specific plane and the plurality of points formed by the plurality of points. The object may be a person, and the skeleton information may include a plurality of points corresponding to the joints and major bones of a person.

A method for recognizing a user's motion using skeleton information includes a step of performing neural network learning using a motion recognition apparatus as a first feature point based on a first plurality of joint points of a human joint point in a training image including a plurality of training operations of a person A step of the motion recognition device acquiring an input image including a human input operation with a camera, a step of recognizing the motion of the input image at a second plurality of joint points at the same position as the first plurality of joint points of the human joint point Extracting a second feature point based on the second feature point, and inputting the second feature point to a neural network formed through neural network learning to determine a training operation corresponding to the input operation.

The feature point may be at least one of an angle between two straight lines formed by three points out of a plurality of points and an angle between a straight line formed by two points out of the three points and a specific plane.

The technique described below can relatively easily recognize the motion by tracking the skeleton information of the person through the image without any other equipment. In addition, the technique described below uses a feature point that can clearly recognize an operation among human skeleton information, and clearly distinguishes an input operation based on a previously prepared standard through neural network machine learning.

1 is an example of a block diagram illustrating a configuration of an operation recognition apparatus for recognizing an operation based on skeleton information.
2 is an example of human skeleton information.
3 is an example showing skeleton information in a depth image including a person.
4 is an example of feature points using some of the skeleton information.
5 is an example of other minutiae points using some of the skeleton information.
6 is an example of a flowchart of a feature point extraction method for motion recognition in an image.
7 is an example of a flowchart of a method of recognizing a user's movement using skeleton information.

The following description is intended to illustrate and describe specific embodiments in the drawings, since various changes may be made and the embodiments may have various embodiments. However, it should be understood that the following description does not limit the specific embodiments, but includes all changes, equivalents, and alternatives falling within the spirit and scope of the following description.

The terms first, second, A, B, etc., may be used to describe various components, but the components are not limited by the terms, but may be used to distinguish one component from another . For example, without departing from the scope of the following description, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component. And / or < / RTI > includes any combination of a plurality of related listed items or any of a plurality of related listed items.

As used herein, the singular " include "should be understood to include a plurality of representations unless the context clearly dictates otherwise, and the terms" comprises & , Parts or combinations thereof, and does not preclude the presence or addition of one or more other features, integers, steps, components, components, or combinations thereof.

Before describing the drawings in detail, it is to be clarified that the division of constituent parts in this specification is merely a division by main functions of each constituent part. That is, two or more constituent parts to be described below may be combined into one constituent part, or one constituent part may be divided into two or more functions according to functions that are more subdivided. In addition, each of the constituent units described below may additionally perform some or all of the functions of other constituent units in addition to the main functions of the constituent units themselves, and that some of the main functions, And may be carried out in a dedicated manner. Therefore, the existence of each component described in the present specification should be interpreted as a function, and for this reason, the configuration of the user motion recognition method using skeleton information described below is limited to the extent that the object of the following description can be achieved It should be clear that this can be different from the corresponding drawings in Fig.

Also, in performing a method or an operation method, each of the processes constituting the method may take place differently from the stated order unless clearly specified in the context. That is, each process may occur in the same order as described, may be performed substantially concurrently, or may be performed in the opposite order.

1 is an example of a block diagram showing the configuration of the motion recognition apparatus 100 for recognizing motion based on skeleton information. In FIG. 1, a person performs a certain operation, and the motion recognition apparatus 100 acquires an image including the user through the first camera 111 and the second camera 112. The motion recognition apparatus 100 acquires an image having depth information with two cameras. The two cameras may be RGB cameras or infrared cameras. Since the process of generating the depth information image is well known, detailed description thereof will be omitted.

In FIG. 1, only two cameras 111 and 112 are shown in the configuration of the motion recognition apparatus 100. As will be described later, the motion recognition apparatus 100 extracts a person (object) and extracts a feature point based on a human skeleton information. Accordingly, the motion recognition apparatus 100 includes a storage medium for storing the acquired image, a central processing unit for image processing, and the like. The motion recognition apparatus 100 may be a kind of computer apparatus. The motion recognition device 100 may be a device such as a game console, a smart TV, a PC, a smart door lock, a sign language translation device, and the like.

The motion recognition apparatus 100 acquires an image including a person who takes a certain action, and extracts a person from the image acquired through the image processing technique. Human extraction uses a technique to extract a specific object from the image except for the background.

Depth information is obtained by transforming disparity information between two images / videos obtained by using two or more cameras using human binocular model into three-dimensional information or measuring three-dimensional depth at a distance using an infrared sensor can do. Depth information is represented as a black and white image with pixel values between [0,255]. The closer the subject is to the reference point of the camera, the brighter the area, and the darker the image becomes. In a depth image, a person is positioned in front of the background and has a characteristic that it is expressed brighter than the background. By reflecting these characteristics, it is possible to distinguish people from images through the mixture of color information and depth information.

The motion recognition apparatus 100 extracts a person from the image and extracts a person's skeleton information using an additional image processing technique. The depth information value obtained by the infrared camera has a flat value in the foreground or background, and recognizes a person having a human skeleton as a human among nearby objects. 2 is an example of human skeleton information.

The skeletal information includes the locations of the main bones involved in the human joint region and the movement of the body. The skeleton information in the arm includes a hand point, a wrist point, an elbow point and a shoulder point, and the skeleton information in the leg includes a foot point, an ankle point, a knee point, and a hip point. The remaining skeleton information includes the head point, the shoulder center point, the spinal point, and the hip center point. FIG. 2 shows all 20 positions as skeleton information.

The motion recognition apparatus 100 can determine the position of each part included in the skeleton information as a three-dimensional coordinate with reference to a certain point.

3 is an example showing skeleton information in a depth image including a person.

FIG. 3 (a) is an example of dividing an object corresponding to a person and skeletonizing the object in a depth image of a person. In Fig. 3 (a), the portion represented by a straight line in the human body corresponds to the result of skeletonization.

Skeletonization is the process of creating a set of lines and curves that summarize the size and shape of an object in binary space. Since there are many ways to define a skeleton, there can be different skeletons in a given object. However, since the image recognized by the motion recognition apparatus 100 is a human image, the anatomical structure of the human being is similar to the result of the skeleton. Skeletonization is performed using erosion operations. The skeleton is an erosion mask with several different directional directions, and the objects in the image are gradually shaved and eroded repeatedly to reveal the substructure of the object. The skeleton information described above can be determined on the basis of the length of the skeleton, the position of the straight line, and the deflection of the straight line based on the information on the structural characteristics of the person. In Fig. 3 (a), skeleton information is indicated by a small point in a straight line. In Fig. 3 (a), the shoulder point, the elbow point and the wrist point are separately shown.

FIG. 3 (b) shows an example in which only the position of the skeleton information is shown in a cross shape in the depth information image. The motion recognition apparatus 100 can determine the skeleton information of the person in the acquired image and then track the position of each skeleton information according to the motion of the person.

The motion recognition apparatus 100 extracts feature points based on the position of the skeleton information. The feature point is a criterion for recognizing the motion of a person.

The motion recognition apparatus 100 can generate high-dimensional feature points by combining the above-described 20 pieces of skeleton information. For example, when there is skeleton information of R = {p1, p2, ... pn}, feature points Z = {z1, z2, ..., pn} are obtained through combinations such as z1 = p1 + p2, , zm} can be generated. As a result, various feature points can be generated using a plurality of skeleton information.

A process of extracting several feature points through FIGS. 4 and 5 will be described. 4 is an example of feature points using some of the skeleton information. Fig. 4 (a) shows an image of a person taking a certain action, and Fig. 4 (b) shows an example of determining a feature point using a part of the positions of skeleton information.

First, it is assumed that the motion recognition apparatus 100 has determined the three-dimensional coordinate value of the skeleton information. In Fig. 4, the skeleton information uses the left shoulder point P1, the left elbow point P2, and the left wrist point P3. The three-dimensional coordinates of each skeleton information are P1 (x1, y1, z1), P2 (x2, y2, z2), and P3 (x3, y3, z3). Three feature points can be obtained as shown below. On the other hand, the x, y, and z axes can be set according to specific criteria.

(1) the angle between two straight lines (u)

P1 and P2 form one straight line (first straight line), and P2 and P3 form one straight line (second straight line). Therefore, the angle u between the first straight line and the second straight line can be used as a characteristic point. The angle u between the first straight line and the second straight line can be calculated by the following equation (1).

)(2) the angle between one straight line and the first plane

)

)를 특징점으로 삼을 수 있다. 제1 평면은 다양한 기준의 평면을 사용할 수 있다. 예컨대, 도 4에서는 z축에 평행한 축을 제1 평면으로 사용하였다. 제1 직선과 제1 평면 사이의 각도(

)는 아래의 수학식 2를 통해 연산할 수 있다.The angle between the first straight line and the first plane formed by P1 and P2

) As a feature point. The first plane may use various reference planes. For example, in FIG. 4, the axis parallel to the z-axis is used as the first plane. The angle between the first straight line and the first plane

) Can be calculated by the following equation (2).

Or an angle between the second straight line formed by P2 and P3 and the first plane may be used as a feature point.

(3) an angle (?) Between one straight line and the second plane

The angle? Between the first straight line and the second plane formed by P1 and P2 can be used as a characteristic point. The second plane may use a plane of various criteria. For example, in FIG. 4, an axis parallel to the y-axis is used as the second plane. The angle? Between the first straight line and the first plane can be calculated by the following equation (3).

Figure 4 shows three characteristic points for the left arm of a person. In the same way, three characteristic points can be extracted for a human right arm.

5 is an example of other minutiae points using some of the skeleton information. FIG. 5 shows an example using skeleton information of a leg. Fig. 5 (a) shows a person image taken with a certain operation, and Fig. 5 (b) shows an example of determining a minutiae using a part of the positions of the skeleton information.

In Fig. 5, the skeleton information uses the right hip point Q1, the right knee point Q2, and the right ankle point Q3. The three-dimensional coordinates of each skeleton information are Q1 (x1, y1, z1), Q2 (x2, y2, z2) and Q3 (x3, y3, z3).

) 및 (3) Q1과 Q2가 형성하는 제1 직선과 제2 평면인 y축에 평행한 평면 사이의 각도(θ)를 특징점으로 결정할 수 있다.(1) an angle (u) between a first straight line formed by Q1 and Q2 and a second straight line formed by Q2 and Q3, (2) a first straight line formed by Q1 and Q2 And the plane parallel to the z-axis, the first plane (

) And (3) an angle? Between the first straight line formed by Q1 and Q2 and the plane parallel to the y-axis which is the second plane.

In addition, three feature points can be extracted in the same way for the left foot.

As described with reference to FIGS. 4 and 5, if three feature points are respectively extracted using the skeleton information of the left arm, the right arm, the left leg, and the right leg, 12 feature points can be provided.

6 is an example of a flowchart of a feature point extraction method 300 for motion recognition in a video. When the feature point extraction is summarized, the motion recognition apparatus first obtains an image having depth information with the camera (310). The motion recognition device extracts a specific object (person) included in the image. The motion recognition apparatus extracts skeleton information of the extracted object (320). The motion recognition apparatus determines a three-dimensional position of a plurality of points used as a reference of a minutia in the skeleton information (330). Finally, the motion recognition device determines at least one of an angle between a straight line formed by a part or all of a plurality of points determining the three-dimensional position and an angle between the straight line and the specific plane as a feature point (340).

Various feature points other than the above-described feature points may also be used. Basically, other points (skull point, shoulder point, spinal point, etc.) of the skeleton information can be used. The skeleton information used for the minutiae may vary depending on which operation is used as an interface. In addition, the characteristic points may be different depending on whether the human motion is an entire body motion or a part of motion such as an arm.

Although the feature point u described above uses a straight line formed by adjacent skeleton information, a straight line between adjacent skeleton information may not necessarily be used. Further, it is possible to use another straight line or plane which is a reference for determining the angle. In other words, it is possible to generate feature points in various combinations.

7 is an example of a flowchart of a method 400 for recognizing a user's motion using skeleton information.

In order for the motion recognition apparatus to recognize the user's operation, reference data for the user's operation is first required. Although various methods can be used, basically, it is explained based on neural network learning during machine learning.

Machine learning can be divided into supervised learning and unsupervised learning depending on the application and data type. The goal of unconditional learning is to identify the type of input pattern and to understand common characteristics between inputs because there is no relation between input and output for learning. On the other hand, supervised learning is a process of learning a function that maps input and output from a dictionary information that is known in advance. In other words, the system learns the function f by providing the input vector x and the correct response y to it as learning data, and learns to infer the output y 'when the new data x' that has not been learned comes in. Therefore, supervised learning techniques are used for user motion recognition.

Neural network learning, which is a type of machine learning, is one of the supervised learning methods in which correct answer Y is learned about X in input data. The motion recognition device receives Label 1 ~ Label N motions corresponding to N motions respectively and inputs 12 minutiae points (input X) for each motion, Create a network. As a result of neural network learning, node structure information and inter-node connection coefficients are obtained.

The motion recognition apparatus extracts feature points using a training image including a training operation of a person. An operation for learning is referred to as a training operation, and an image including a training operation is referred to as a training image. The extracted feature points are generated using a plurality of joint points in the skeleton information as described above. The plurality of joint points used in the learning process is called the first plurality of joint points. The feature points extracted using the first plurality of joint points in the learning process are referred to as first feature points. The first feature point may be twelve feature points as described above. The motion recognition apparatus performs a neural network learning based on the first feature point in a plurality of training operations (410).

If the neural network learning for the training operations is not completed (No at 420), the neural network learning is repeatedly performed (410). If the neural network learning for the training operations is completed (Yes at 420) Wait for video.

The motion recognition apparatus acquires an input image requiring recognition of the motion using two cameras (430). The reason for using two cameras is to generate images with depth information.

The motion recognition device extracts the object (person) included in the projection from the input image and extracts the skeleton information from the extracted person. A plurality of joint points are determined in an object called a person (440). The plurality of joint points determined in the input image are referred to as a second plurality of joint points. The second plurality of joint points should be the same (corresponding) position as the first plurality of joint points used in the learning phase.

The motion recognition apparatus determines a feature point using a plurality of points of the second plurality of joint points (450). The feature point for the input image is called the second feature point. The second feature point corresponds to the first feature point. If the first feature points are the 12 feature points described in FIGS. 4 and 5, the second feature points should be the same 12 feature points.

The motion recognition device finally enters the second feature point into the neural network network and recognizes the final determined motion as an input motion. For example, if a neural network for N actions is created in the learning process, the motion recognition device determines the input action as one of the N actions.

The motion recognition device extracts 12 feature points from the input image in the same procedure as the learning process and inputs them to the learned neural network using the obtained parameter set (in the case of the neural network, the structure of the node and the connection coefficient between the nodes) And one of Label1 to Label N motions in the final output node. The neural network information obtained by recognizing the motion through the neural network network while recognizing the new operation is fed back to 410 to update the neural network (470).

It should be noted that the present embodiment and the drawings attached hereto are only a part of the technical idea included in the above-described technology, and those skilled in the art will readily understand the technical ideas included in the above- It is to be understood that both variations and specific embodiments which can be deduced are included in the scope of the above-mentioned technical scope.

100: motion recognition device 111: first camera
112: Second camera

Claims (12)

The motion recognition device acquiring an image having depth information with a camera;
Extracting an object included in the image and extracting skeleton information of the object;
The motion recognition device determining coordinates of a plurality of points in the skeleton information; And
Wherein the motion recognition device determines an angle formed by the plurality of points and an angle formed by the plurality of points and the specific plane as feature points. The method according to claim 1,
Wherein the object is a person and the skeleton information includes a plurality of points corresponding to a joint and a main bone of a person. The method according to claim 1,
Wherein the feature points include at least one of an angle between two straight lines formed by three points among the plurality of points and an angle between a straight line formed by two points out of the three points and a specific plane, Extraction method. The method according to claim 1,
For the shoulder point, elbow point and wrist point of one of the plurality of points
Wherein the characteristic point is defined by an angle between a first straight line formed by the shoulder point and the elbow point, a second straight line formed by the elbow point and the wrist point, an angle formed by the first straight line and the first plane, And extracting a feature point for motion recognition in an image that is at least one of an angle formed by a straight line and an angle formed by the second plane. The method according to claim 1,
The hip, knee, and ankle points of one of the plurality of points
Wherein the characteristic points include an angle between a first straight line formed by the hip point and the knee point, a second straight line formed by the knee point and the ankle point, an angle formed by the first straight line and the first plane, And extracting a feature point for motion recognition in an image that is at least one of an angle formed by a straight line and an angle formed by the second plane. Performing neural network learning using the motion recognition apparatus as a first feature point based on a first plurality of joint points of a human joint point in a training image including a plurality of training operations of a person;
The operation recognition device acquiring an input image including a human input operation with a camera;
Extracting a second feature point based on a second plurality of joint points at the same position as the first plurality of joint points of the human joint points in the input image; And
And inputting the second feature point to a neural network formed through the neural network learning to determine a training operation corresponding to the input operation. The method according to claim 6,
The motion recognition apparatus extracts the person from each of the training image and the input image, performs skeletonization of the extracted human image, and generates the skeletonized object based on human skeleton information, And determining a plurality of joint points corresponding to the region. The method according to claim 6,
The step of performing
The motion recognition device determining coordinates for the first plurality of points;
Determining, by the motion recognition apparatus, the angle formed by the first plurality of points and the angle formed by the plurality of points and the specific plane as the first feature point; And
Performing a neural network learning based on the first characteristic point to prepare a neural network,
Wherein the performing of the plurality of training operations comprises using skeleton information that is repeatedly performed for each of the plurality of training operations. The method according to claim 6,
The extracting step
The motion recognition device determining coordinates for the second plurality of points; And
And determining the angle formed by the second plurality of points and the angle formed by the plurality of points and the specific plane as the second feature point by the motion recognition apparatus. The method according to claim 6,
I) the shoulder point of the left arm, the elbow point of the left arm and the wrist point of the left arm, ii) the shoulder point of the right arm, the elbow point of the right arm and the wrist point of the right arm, iii) A hip point of the leg, a knee point of the left leg and an ankle point of the left leg, and iv) a hip point of the right leg, a knee point of the right leg, and an ankle point of the right leg. The method according to claim 6,
Wherein the characteristic points include an angle between a first straight line formed by the shoulder point and the elbow point, a second straight line formed by the elbow point and the wrist point, an angle formed by the first straight line and the first plane, Wherein the skeleton information is at least one of angles formed by two planes. The method according to claim 6,
Wherein the characteristic points include an angle between a first straight line formed by the hip point and the knee point, a second straight line formed by the knee point and the ankle point, an angle formed by the first straight line and the first plane, Wherein the skeleton information is at least one of angles formed by two planes.

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