CN111353347A

CN111353347A - Motion recognition error correction method, electronic device, and storage medium

Info

Publication number: CN111353347A
Application number: CN201811578235.3A
Authority: CN
Inventors: 冯伟; 孟庆伟
Original assignee: Shanghai Myshape Information Technology Co ltd
Current assignee: Shanghai Shibeisi Fitness Management Co ltd
Priority date: 2018-12-21
Filing date: 2018-12-21
Publication date: 2020-06-30
Anticipated expiration: 2038-12-21
Also published as: CN111353347B

Abstract

The invention provides a motion recognition error correction method, an electronic device and a storage medium, comprising the following steps: a. determining a target action; b. acquiring two-dimensional video data by using a two-dimensional video acquisition device, and generating a two-dimensional skeleton action model in real time; c. taking the collected two-dimensional skeleton motion model as a motion to be measured to form a matching group; d. forming a part matching group in each matching group; e. for each part matching group, acquiring action recognition feedback at least according to the recognition item of the random part action in the target part action; f. and integrating the action recognition feedback of at least one matching group to obtain the action recognition feedback of the action to be detected. The method and the device provided by the invention can realize real-time identification and error correction of actions such as body building, dancing, movement and the like in a two-dimensional scene.

Description

Motion recognition error correction method, electronic device, and storage medium

Technical Field

The invention relates to the technical field of computer application, in particular to a motion recognition error correction method, electronic equipment and a storage medium.

Background

Human motion capture and recognition methods are very widely used in today's society, for example: intelligent monitoring, human-computer interaction motion sensing games, video retrieval and the like.

Human motion detection recognition, which is a transition from traditional RGB-based video sequences to today's popular RGB-D video sequences, has been developed as an important feature. The traditional motion trail capture is usually based on a detection algorithm of characteristic points, and different characteristic point detection methods can obtain completely different motion trails. Meanwhile, because the retrieval of the feature points in different frames is very unstable, and the feature points are often discontinuous in the whole video sequence, a histogram-based statistical method is mostly adopted for the feature point trajectory method, and after the whole video sequence is calculated and counted, classifiers such as a support vector machine and the like are adopted for classification.

The matching calculation method of the video sequences has large calculation amount, cannot respond immediately and cannot be suitable for man-machine interaction at the civil level. Therefore, the prior art has difficulty in meeting the requirement of a system for real-time feedback of whether the action is wrong or not for human-computer interaction of fitness identification and error correction.

Meanwhile, the prior art is lack of a matching and identifying algorithm for two-dimensional bone data, and cannot cope with the application scene of a two-dimensional video collector.

Disclosure of Invention

In order to overcome the defects in the prior art, the invention provides a motion recognition error correction method, electronic equipment and a storage medium, so as to realize real-time motion recognition and error correction of fitness, dance, movement and the like.

According to an aspect of the present invention, there is provided a motion recognition error correction method, including:

a. determining target actions, wherein the target actions at least comprise one target action stage, each target action stage is divided into a plurality of target part actions, the target part actions comprise actions of dividing 5 body parts according to the body parts and at least one random part action, and the body parts comprise: a left arm, a right arm, a left leg, a right leg, and a torso, the random part being comprised of selected at least two skeletal points in the body part,

the random part action at least corresponds to one or more process-oriented identification items, each identification item comprises an identification object, an identification parameter, an identification rule and a standard skeleton point coordinate base, the identification object comprises a vector formed by at least two skeleton points of the random part corresponding to the process-oriented identification items, and the standard skeleton point coordinate base stores standard coordinates of all the skeleton points in the target action according to the time sequence;

b. acquiring two-dimensional video data by using a two-dimensional video acquisition device, and generating a two-dimensional skeleton action model in real time;

c. taking the collected two-dimensional skeleton motion model as a motion to be detected, dividing the motion to be detected into at least one motion stage to be detected according to the time of the target motion stage of the target motion, and forming a matching group by the target motion stage and the motion stage to be detected with corresponding time;

d. in each matching group, dividing the action stage to be detected into corresponding action of the part to be detected according to the action of the target part in the target action stage, and forming a part matching group by the action of the part to be detected in the action stage to be detected and the action of the target part in the corresponding target action stage;

e. for each part matching group, at least acquiring an identification item of random part action in target part action, acquiring a vector formed by at least two selected bone points in the action of the part to be detected according to the two-dimensional bone action model, and performing matching calculation on the vector of the random part action and a standard vector formed by corresponding standard coordinates in a standard bone point coordinate library so as to compare the vector with a vector threshold set by the identification parameters to acquire action identification feedback;

f. and integrating the action recognition feedbacks of the plurality of matching groups to obtain the action recognition feedback of the action to be detected.

Optionally, matching the vector of the random part motion with a standard vector formed by corresponding standard coordinates in a standard bone point coordinate library to compare with a vector threshold set by the identification parameter comprises:

calculating standard vectors formed by corresponding standard coordinates in a standard skeleton point coordinate library

(x_ai，y_ai) Vector of motion of random part

(x_bi，y_bi) Cosine of angle θ between:

vector quantity

And vector

The cosine value of the included angle theta is used for comparing with the vector threshold value set by the identification parameter.

Optionally, the matching calculation of the vector of the random part motion and a standard vector formed by corresponding standard coordinates in a standard bone point coordinate library to compare with the vector threshold set by the identification parameter further includes:

judging whether the corresponding standard coordinates in the standard skeleton point coordinate library are two-dimensional coordinates or not;

if so, performing matching calculation on the vector of the random part action and a standard vector formed by corresponding standard coordinates in a standard skeleton point coordinate library;

if not, converting the corresponding standard coordinates in the standard skeleton point coordinate library into two-dimensional coordinates, and performing matching calculation with the vector of the random part action.

Optionally, for the process-oriented identification item corresponding to the random part action:

the identification parameters also include a starting amplitude threshold and an achieved amplitude threshold,

the initial amplitude threshold is used for judging whether the action of the part to be detected starts;

the achievement amplitude threshold is used for judging whether the action of the part to be detected is finished or not.

Optionally, the random part motion further corresponds to one or more distance-oriented recognition items, and for the distance-oriented recognition items:

the identification object comprises a distance between at least two bone points of the random portion;

the identification parameter sets a distance threshold; and

the identification rule comprises that the identification object of the action of the part to be detected is always greater than or equal to a distance threshold value set by the identification parameter in the motion process.

Optionally, the target action at least includes a plurality of target action phases with a precedence order, and step f includes:

when the action identification feedback of the previous target action stage and the corresponding action stage to be tested is not achieved, the action identification feedback achieved by the action of the subsequent target action stage and the corresponding action stage to be tested is invalid.

Optionally, each of the body parts includes three bone points and three vectors, the body part motion corresponds to one or more process-oriented or displacement-oriented recognition items, each recognition item includes a recognition object, a recognition parameter and a recognition rule, and the recognition object includes at least one bone point of the three bone points; at least one of the three vectors; and one or more of an angle between two of the three vectors, the process-oriented identification term also comprising the library of standard bone point coordinates.

Optionally, during the movement of the motion, the sampling frequency of the motion of the part to be detected is equal to the sampling frequency of the standard bone point coordinate library.

Alternatively,

the left arm includes: the left wrist joint point, the left elbow joint point, the left shoulder joint point, a first vector formed from the left shoulder joint point to the left elbow joint point, a second vector formed from the left elbow joint point to the left wrist joint point, a third vector formed from the left shoulder joint point to the left wrist joint point and an included angle between the first vector and the second vector;

the right arm includes: a right wrist joint point, a right elbow joint point, a right shoulder joint point, a first vector formed from the right shoulder joint point to the right elbow joint point, a second vector formed from the right elbow joint point to the right wrist joint point, a third vector formed from the right shoulder joint point to the right wrist joint point, and an included angle between the first vector and the second vector;

the trunk includes: the head part center, the spine center of the neck, the spine center of the trunk, a first vector formed from the head part center to the spine center of the neck, a second vector formed from the spine center of the neck to the spine center of the trunk, a third vector formed from the head part center to the spine center of the trunk, and an included angle formed by the first vector and the second vector;

the left leg includes: the left ankle joint point, the left knee joint point, the left hip joint point, a first vector formed from the left hip joint point to the left knee joint point, a second vector formed from the left knee joint point to the left ankle joint point, a third vector formed from the left hip joint point to the left ankle joint point, and an included angle between the first vector and the second vector;

the right leg includes: the right ankle joint point, the right knee joint point, the right hip joint point, a first vector formed from the right hip joint point to the right knee joint point, a second vector formed from the right knee joint point to the right ankle joint point, a third vector formed from the right hip joint point to the right ankle joint point, and an included angle between the first vector and the second vector.

According to still another aspect of the present invention, there is also provided an electronic apparatus, including: a processor; a storage medium having stored thereon a computer program which, when executed by the processor, performs the steps as described above.

According to yet another aspect of the present invention, there is also provided a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps as described above.

Compared with the prior art, on one hand, the method realizes the matching calculation of the two-dimensional bone data, so that the action recognition can be applied to a scene of two-dimensional video acquisition; on the other hand, each action to be detected which is collected in real time is simplified according to the body structure, the action is divided into the actions of the part to be detected by taking three skeleton points as a unit, the action of the part to be detected is identified by a process-oriented identification item, and a vector formed by the skeleton points which are collected in real time and a vector formed by the coordinates of the skeleton points in a standard skeleton point coordinate library in the process-oriented identification item is simply calculated to be compared with a set vector threshold value, so that the process calculation amount of the setting and matching identification of the skeleton points and the vectors is small, the real-time feedback can be realized, and the phenomenon of feedback delay can not be generated; in still another aspect, the invention can also realize the recognition of random parts except for the predetermined five body parts, and the flexibility of motion recognition is increased.

Drawings

The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

FIG. 1 shows a flow diagram of a method of motion recognition error correction according to an embodiment of the invention;

FIG. 2 shows a schematic diagram of a bone model according to an embodiment of the invention;

figures 3 to 7 show schematic views of 5 body parts according to an embodiment of the invention;

FIG. 8 illustrates a comparison of a standard vector formed by bone points from a standard bone point coordinate base and a real-time acquisition vector according to an embodiment of the present invention;

FIGS. 9 and 10 are diagrams illustrating an angle between the normal vectors formed by the bone points in the normal bone point coordinate base and an angle between the real-time collection vectors according to an embodiment of the present invention;

FIG. 11 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the disclosure.

Fig. 12 schematically illustrates an electronic device in an exemplary embodiment of the disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams depicted in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

Referring first to fig. 1, fig. 1 shows a flow chart of a motion recognition error correction method according to an embodiment of the invention. Fig. 1 shows 6 steps in total:

first, step S110: determining a target action, wherein the target action at least comprises a target action stage, each target action stage is divided into a plurality of target part actions, and the target part actions comprise 5 body part actions divided according to body parts and at least one random part action.

In some embodiments, the target action may be determined by displaying a workout video. Specifically, the fitness video comprises a plurality of target actions, and each target action is associated with the playing time of the fitness video. In other embodiments, the user may directly select the target action.

Specifically, in the present case, 15 skeletal points are set for each human body (see fig. 2), and the 15 skeletal points are: head center 211, neck center (e.g., spinal center of neck) 212, torso center 213 (e.g., spinal center of torso), left shoulder joint point 221, left elbow joint point 222, left wrist joint point 223, right shoulder joint point 231, right elbow joint point 232, right wrist joint point 233, left hip joint point 241, left knee joint point 242, left ankle joint point 243, right hip joint point 251, right knee joint point 252, right ankle joint point 253.

In the present case, the 15 skeletal points are divided into five body parts by taking 3 skeletal points as units: the torso (see fig. 3), the left arm (see fig. 4), the right arm (see fig. 5), the left leg (see fig. 6), and the right leg (see fig. 7). Vectors are formed among the skeleton points in each body part, and included angles are formed among the vectors.

Specifically, the torso (see fig. 3) includes a head center 211, a spine center 212 of the neck, a spine center 213 of the torso, a first vector 214 formed from the head center 211 to the spine center 212 of the neck, a second vector 215 formed from the spine center 212 of the neck to the spine center 213 of the torso, a third vector 216 formed from the head center 211 to the spine center 213 of the torso, and an angle 217 formed by the first vector 214 and the second vector 215.

The left arm (see fig. 4) includes a left wrist joint point 223, a left elbow joint point 222, a left shoulder joint point 221, a first vector 224 formed from the left shoulder joint point 221 to the left elbow joint point 222, a second vector 225 formed from the left elbow joint point 222 to the left wrist joint point 223, a third vector 226 formed from the left shoulder joint point 221 to the left wrist joint point 223, and an angle 227 between the first vector 224 and the second vector 225.

The right arm (see fig. 5) includes a right wrist joint point 233, a right elbow joint point 232, a right shoulder joint point 231, a first vector 234 formed from the right shoulder joint point 231 to the right elbow joint point 232, a second vector 235 formed from the right elbow joint point 232 to the right wrist joint point 233, a third vector 236 formed from the right shoulder joint point 231 to the right wrist joint point 233, and an angle 237 between the first vector 234 and the second vector 235.

The left leg includes (see fig. 6) a left ankle joint point 243, a left knee joint point 242, a left hip joint point 241, a first vector 244 formed from left hip joint point 241 to left knee joint point 242, a second vector 245 formed from left knee joint point 242 to left ankle joint point 243, a third vector 246 formed from left hip joint point 241 to left ankle joint point 243, and an angle 247 between the first vector 244 and the second vector 245.

The right leg includes (see fig. 7) a right ankle joint point 253, a right knee joint point 252, a right hip joint point 251, a first vector 254 formed from right hip joint point 251 to right knee joint point 252, a second vector 255 formed from right knee joint point 252 to right ankle joint point 253, a third vector 256 formed from right hip joint point 251 to right ankle joint point 253, and an angle between the first vector 254 and the second vector 255.

Less representative joint points are set as skeleton points to reduce the amount of calculation in motion recognition and error correction.

The target action is broken down into five body parts: left arm, right arm, left leg, right leg and torso. Each body part comprises three skeletal points as shown in fig. 3 to 7, three vectors formed by the three skeletal points, and an included angle between two of the three vectors.

To increase the flexibility of motion recognition, the target motion may further comprise at least one random part motion, the random part being constituted by at least two selected bone points in said body part, such as selected

bone points

212 and 223 in fig. 2, and the random part being formed by

bone points

212 and 223. The random part is not limited to this, and any at least two bone points may form the random part, so that on the basis of five body parts, more dimensional motion recognition may be achieved.

The random part action at least corresponds to one or more process-oriented identification items, and each identification item comprises an identification object, an identification parameter, an identification rule and a standard skeleton point coordinate library. In the identification item corresponding to the process, the identification object comprises a vector formed by at least two bone points of the random part. The identification parameters include a set vector threshold. The identification rule includes that the similarity between a vector (identification object) formed by at least two bone points of the random part and a standard vector formed by corresponding standard coordinates in a standard bone point coordinate library is required to be greater than or equal to a set vector threshold (identification parameter) in the motion process, and if the similarity between the vector (identification object) formed by at least two bone points of the random part and the standard vector formed by corresponding standard coordinates in the standard bone point coordinate library is smaller than the set vector threshold (identification parameter), an error is reported (the reported error can be stored as the identification parameter in advance).

In a specific embodiment, the vector of the random part motion and a standard vector formed by corresponding standard coordinates in a standard bone point coordinate library are subjected to matching calculation to be compared with a vector threshold set by the identification parameter through the following steps:

(x_ai，y_ai) Vector of motion of random part

(x_bi，y_bi) Cosine of angle θ between:

vector quantity

And vector

The cosine value of the included angle theta is used for comparing with the vector threshold value set by the identification parameter. For example, when bone points 212 and 223 form random sites, the vector

And vector

The vectors formed by the bone points 212 and 223 acquired in real time and the vectors formed by the bone points 212 and 223 in the standard bone point coordinate base are respectively.

Further, in the invention, the two-dimensional video data collected in real time generates a two-dimensional skeleton motion model, and the coordinates in the standard skeleton point coordinate library can be three-dimensional coordinates. And if so, matching and calculating the vector of the random part action and a standard vector formed by corresponding standard coordinates in a standard skeleton point coordinate library. If not, converting the corresponding standard coordinates in the standard skeleton point coordinate library into two-dimensional coordinates, and performing matching calculation with the vector of the random part action.

In a specific embodiment, for the process-oriented identification item corresponding to the random part motion, the identification parameter may further include an initial amplitude threshold and an achievement amplitude threshold, where the initial amplitude threshold is used to determine whether the motion of the part to be detected starts; the achievement amplitude threshold is used for judging whether the action of the part to be detected is finished or not. Specifically, the starting amplitude and the achievement amplitude are based on the position on the action time axis. In specific implementation, the frame number can be used to determine the initial amplitude and the achieved amplitude. For example: assuming that an action has 20 frames of data in the standard bone point coordinate library, assuming that the initial amplitude threshold is set to 0.2, the amplitude threshold is set to 0.8. then the action is determined to start when the matching degree of any frame data between the random part action of the actual action of the user and the 0 th to 4 th (i.e. 20 x 0.2) frames in the standard bone point coordinate library is the highest (within the vector threshold range). When the user action is started and the random part action fails to match with the standard skeleton point coordinate library in the motion process, once the matching degree of any frame data between the random part action of the user action and the 16 th (namely 20 x 0.8) -20 th frames in the standard skeleton point coordinate library is the highest (within the range of the vector threshold), the action is determined to be achieved. The foregoing is merely an illustrative description of implementations of the invention and is not intended to be limiting thereof.

In a specific embodiment, the random part motion further corresponds to one or more distance-oriented recognition items. For the distance-oriented recognition item, the recognition object comprises the distance between at least two bone points of the random part. The identification parameter sets a distance threshold. The identification rule comprises that the identification object of the action of the part to be detected is always larger than or equal to the range of the distance threshold set by the identification parameter in the motion process. In the distance recognition, when the recognition object moving at the part to be detected is always greater than or equal to the distance threshold value set by the recognition parameter in the moving process, the movement is achieved; and when the identification object moving at the part to be detected is smaller than the distance threshold set by the identification parameter in the moving process, an error is reported. In the negative distance recognition, when the recognition object moving at the part to be detected is greater than or equal to the distance threshold set by the recognition parameter at any time in the moving process, an error is reported.

The above description is only illustrative of the embodiment of random part motion recognition error correction in the present invention, and the present invention is not limited thereto. The following will describe an embodiment mode of recognition and error correction of body part motion in the present invention.

The at least one body part action corresponds to one or more process-oriented or displacement-oriented recognized terms. Each identification item comprises an identification object, an identification parameter and an identification rule, wherein the identification object comprises at least one of the three skeleton points of the part action; at least one of the three vectors; and one or more of an angle between two of the three vectors.

The process-oriented identification item needs to be matched with the vector collected in real time through a standard skeleton point coordinate library so as to judge whether the identification item is met. The standard bone point coordinate library stores the coordinates of at least one bone point of the part motion in a time sequence with a sampling frequency. For example, for the left leg movement of the push-up, at least the coordinates of the bone points 221, 222, and 223 of the left arm are stored in time series at a sampling frequency of 5 times/second, whereby the first vector 224 and the second vector 225 (and the angle 227) formed by the bone points 221, 222, and 223 can be known.

Specifically, the identification items facing the process comprise track identification, negative track identification and hold identification; the identification items facing displacement include displacement identification and negative displacement identification.

And the track identification is used for identifying whether the part moves according to a preset track, and if the part does not move according to the preset track, an error is prompted. The identification object comprises at least one vector in the three vectors and/or an included angle between two vectors in the three vectors. The identification parameter sets one or more threshold values corresponding to the identification object. The threshold value comprises a vector threshold value of the three vectors and an included angle threshold value of the included angle, and the identification parameter determines to adopt the vector threshold value and/or the included angle threshold value according to the identification object.

Specifically, the vector threshold and the included angle threshold are used to determine whether the vectors (and included angles) collected in real time match the standard vectors (and included angles between the standard vectors) formed by the standard bone points in the standard bone point coordinate library. For example, referring to FIG. 8, for the vector threshold, the vectors from skeleton point 222 to skeleton point 293 of a body part motion are collected in real time

(x_bi，y_bi) Finding corresponding bone points 222 to 223 corresponding to the time in a standard bone point coordinate library according to the time to form a vector

(x_ai，y_ai) Calculating the vector in the standard skeleton point coordinate library

(x_ai，y_ai) Vector of body part motion acquired in real time

(x_bi，y_bi) Cosine of angle θ between:

vector quantity

And vector

The cosine value of the included angle theta (cosine value is-1 to 1) is used for comparing with the vector threshold value set by the identification parameter. The vector threshold may be set to 0.8, the corresponding vector

And vector

When the cosine value of the included angle theta is greater than or equal to 0.8, the two vectors are considered to be matched. The vector can be determined by comparing the vector threshold with the calculated cosine value

(x_bi，y_bi) Whether it is within the vector threshold.

For example, in an embodiment where an angle threshold is set, the standard bone point coordinate library stores at least standard bone points in chronological order and may include an angle between a standard vector formed by the standard bone points and the angle between the standard vectors, the first and second vectors of body part motion may be calculated from the two vectors or may be stored directly in the standard bone point coordinate library, referring to fig. 9 and 10, an angle threshold is used to compare an angle 297 α between a first vector 294 of (bone point 292 to bone point 291) and a second vector 295 of (bone point 292 to bone point 293) of the site motion acquired in real time to a ratio α/β of an angle 227 β between a first vector 224 of (bone point 222 to bone point 221) and a second vector 225 of (bone point 222 to bone point 223) of the site motion acquired in real time in the standard bone point coordinate library to determine whether the angle of the site motion acquired in real time is within the range of the angle threshold.

Furthermore, the identification parameters of the track identification also comprise an initial amplitude threshold value and an achievement amplitude threshold value, wherein the initial amplitude threshold value is used for judging whether the part action starts or not, and the achievement amplitude threshold value is used for judging whether the part action finishes or not to complete achievement of the amplitude. Specifically, the starting amplitude and the achievement amplitude are based on the position on the action time axis. In specific implementation, the frame number can be used to determine the initial amplitude and the achieved amplitude. For example: assuming that an action has 20 frames of data in the standard bone point coordinate library, assuming that the initial amplitude threshold is set to 0.2, the amplitude threshold is achieved to be 0.8. then the action is considered to begin when the matching degree of any frame of data between the actual action of the user and the 0 th-4 th (i.e. 20 x 0.2) frames in the standard bone point coordinate library is the highest (within the vector threshold). When the action of the user is started and the matching with the standard bone point coordinate base is not failed in the motion process, once the matching degree of any frame data between the action of the user and the 16 th (namely 20 x 0.8) to 20 th frames in the standard bone point coordinate base is the highest (within the range of the vector threshold value), the action is determined to be achieved. The foregoing is merely an illustrative description of implementations of the invention and is not intended to be limiting thereof.

The recognition rules of the track recognition include achievement rules and optionally different error rules corresponding to the set recognition objects and recognition parameters. The achievement rule of the track recognition is that the recognition object of the part action starts from the position represented by the initial amplitude threshold value and the recognition objects are all within the set vector threshold value and/or included angle threshold value; when the recognition objects of the part action reach the position represented by the amplitude threshold value from the position represented by the initial amplitude threshold value, the recognition objects are all within the set vector threshold value and/or included angle threshold value; and the recognition objects of the part action reach the position represented by the achievement amplitude threshold value and are all within the set vector threshold value and/or included angle threshold value. Different error rules for track identification include: an out of corresponding vector threshold error (e.g., the large arm or thigh represented by vector one is out of threshold); an angle threshold error is exceeded (e.g., an angle at the elbow or an angle at the knee represented by the angle exceeds a threshold); and insufficient amplitude error. The identification rule with the amplitude being not wrong enough is that the identification object of the part action starts from the position represented by the initial amplitude threshold value and the identification objects are all within the set vector threshold value and/or included angle threshold value; when the recognition objects of the part action reach the position represented by the amplitude threshold value from the position represented by the initial amplitude threshold value, the recognition objects are all within the set vector threshold value and/or included angle threshold value; and the recognition objects of the part action do not reach the position represented by the achievement amplitude threshold value and are all within the set vector threshold value and/or included angle threshold value.

And the negative track identification is used for identifying whether the part moves according to a preset track, and if the part moves according to the preset track, an error is prompted. For negative trajectory recognition, which is similar to trajectory recognition, the recognition object comprises at least one of the three vectors and/or an angle between two of the three vectors (preferably, an angle between the first vector and the second vector). And setting one or more thresholds for the identification parameters of the negative track identification, wherein the thresholds comprise vector thresholds of the three vectors and an included angle threshold of the included angle, and the identification parameters determine to adopt the vector thresholds and/or the included angle thresholds according to the identification object. The negative track recognition is different from the track recognition in that the negative track recognition achievement rule is as follows: the identification object of the part action starts from the position represented by the initial amplitude threshold value and is within the set vector threshold value and/or included angle threshold value; when the recognition objects of the part action reach the position represented by the amplitude threshold value from the position represented by the initial amplitude threshold value, the recognition objects are all within the set vector threshold value and/or included angle threshold value; the recognition objects of the part action reach the position represented by the achievement amplitude threshold value and are all within the set vector threshold value and/or included angle threshold value; and there is currently a state in which recognition other than negative recognition and hold recognition is in progress (in other words, the trajectory or displacement amplitude is growing). When the rule is reached, a track error is prompted. In other words, if the recognition object is not always within the threshold range set by the recognition parameter during the movement of the body part, and the motion of the part represented by the recognition object generates a trajectory and/or displacement during the movement, an error will not be presented.

The hold recognition is used to identify whether the motion of the part is kept in a certain state (for example, kept upright or kept at a bending angle) during the motion, and if the motion is not kept in the state, an error is prompted. The identification object kept identified comprises at least one vector in the three vectors and/or an included angle between two vectors in the three vectors. And setting one or more thresholds according to the identification parameters, wherein the thresholds comprise vector thresholds of the three vectors and an included angle threshold of the included angle, and the identification parameters determine to adopt the vector thresholds and/or the included angle thresholds according to the identification object. The achievement rule for keeping identification is: the recognition target of the part motion is always within the set vector threshold and/or included angle threshold. If the achievement rule of the keeping identification is not reached, an error corresponding to the keeping identification is prompted.

For displacement recognition and negative displacement recognition, although the displacement recognition and the negative displacement recognition are described as recognition items facing displacement instead of object, the displacement recognition and the negative displacement recognition actually need to recognize whether the part action is in a continuous motion state, and if the part action is not in the continuous motion state, the recognition is interrupted, and an error is directly prompted; or to re-identify from the current location.

And the displacement identification is used for judging whether the identified object reaches the preset displacement direction and displacement distance, and if not, prompting an error. The recognition object of the displacement recognition includes one of three bone points. Preferably, one skeletal point of the site action is specified. The identification parameters set displacement distance, displacement direction (the displacement direction can be mapped to the positive direction of the X axis, the negative direction of the X axis, the positive direction of the Y axis and the negative direction of the Y axis in the two-dimensional coordinates, and the specific displacement direction does not need to be calculated) and initial amplitude threshold values. The starting amplitude threshold of the displacement is a value in the range of 0 to 1. For example, the starting amplitude threshold may be set to 0.2 and represent that the site action or displacement recognition begins when the displacement of a given bone point exceeds 20% of the set displacement distance. The recognition rules of displacement recognition include an achievement rule and optionally different error rules. The achievement rule of the displacement identification is that the moving direction of the appointed bone point is consistent with the displacement direction set in the identification parameter, and the displacement distance of one continuous motion is more than or equal to the displacement distance set in the identification parameter. Different error rules include that when the displacement of the specified bone point does not exceed the initial amplitude threshold value, the initial action amplitude is not enough; and if the displacement amplitude of the appointed bone point exceeds the initial amplitude threshold value, the moving direction of the appointed bone point is consistent with the displacement direction set in the identification parameter, and the displacement distance of one continuous motion is less than the displacement distance set in the identification parameter, the achievement amplitude is not enough.

And the negative displacement identification is used for judging whether the identified object reaches the preset displacement direction and displacement distance, and if so, prompting an error. Similar to displacement recognition, the recognition object includes one of three bone points. Preferably, one skeletal point of the site action is specified. The identification parameters set displacement distance, displacement direction (the displacement direction can be mapped to the positive direction of the X axis, the negative direction of the X axis, the positive direction of the Y axis and the negative direction of the Y axis in the two-dimensional coordinates) and initial amplitude threshold values. The achievement rule of the negative displacement recognition is that the moving direction of the specified bone point coincides with the displacement direction set in the recognition parameter, the displacement distance of one continuous motion is equal to or greater than the displacement distance set in the recognition parameter, and there is a state in which recognition other than the negative recognition and the hold recognition is currently in progress (in other words, the trajectory or the displacement amplitude is increasing). When the rule is reached, a track error is prompted. In other words, if the recognition object does not move in the displacement direction set by the recognition parameter or the movement distance is greater than the displacement distance set by the recognition parameter during the movement of the body part, no error is indicated.

In the above embodiments, the difficulty factor may be increased, for example, the product of the difficulty factor and the achievement condition for each action may be used as the achievement condition for actions with different difficulties.

The identification item is set for at least one part action of an action, the at least one part action and the identification item of the at least one part action are used as an action file of the action, and the action file and the action number are stored in the standard action database in a correlation mode.

In one embodiment, for a deep squat action, it sets the identification terms for the torso, left leg and right leg. The identification items of the trunk include a hold identification and a displacement identification. In the trunk keeping identification, the identification object is only a first vector from the head center to the spine center of the neck, the parameters of the first vector are set correspondingly, and a standard skeleton point coordinate base of skeleton points of the trunk in the deep squatting process is stored for subsequent matching. When the first vector of the trunk acquired in real time exceeds the threshold value of the first vector, the body is not kept upright, and an error is prompted. Here, due to the characteristics of the trunk, when the first vector from the center of the head to the center of the spine of the neck remains upright, the second vector from the center of the spine of the neck to the center of the spine of the trunk can be generally directly determined to also remain upright, and only a threshold value of one vector is set, so as to reduce the subsequent calculation amount and improve the subsequent real-time error correction efficiency.

In the displacement recognition of the trunk, the recognition object is a skeleton point at the center of the spine of the trunk, and the corresponding recognition parameters are a predetermined displacement distance and a predetermined displacement direction (the direction is the negative direction of the Y axis) of the skeleton point. When the spine center of the torso moves more than a predetermined distance in the negative Y-axis direction, this identification of the motion of the part is indicated. If the spine center of the trunk does not move along the Y-axis negative direction for more than a preset displacement distance, the amplitude of the part motion is not enough.

The left leg is provided with negative displacement recognition for reminding the deep squatting middle knee not to exceed the toe. In the negative displacement recognition of the left leg, the recognition target is a joint point of the left knee, and the recognition parameters are a predetermined displacement distance, a predetermined displacement direction (the direction is the positive X-axis direction), and a start amplitude threshold. When the left knee moves more than a preset displacement distance along the positive direction of the X axis, the prompt shows that the part moves wrongly. When the left knee does not move more than the predetermined displacement distance in the positive X-axis direction, this recognition of the motion of the part is achieved. The identification item of the right leg is the same as that of the left leg, and is not described herein.

In some embodiments, the stages may be divided for each action. For example, for deep squats, squats and uprisals may be divided into two stages. In some embodiments, the movement of the back and forth for squatting, push-up, etc. can be set and identified for only one course in the middle of the back and forth. For example, the setting of the identification item and the identification error correction are only carried out on the action of squatting deeply; the setting of the identification item and the identification error correction are only carried out on the action during the push-up and the push-up, thereby further reducing the calculation amount of the action identification and increasing the real-time performance of the error correction.

Step S120: and acquiring two-dimensional video data by using a two-dimensional video acquisition unit, and generating a two-dimensional skeleton action model in real time.

In one embodiment, the sampling frequency of the real-time acquisition may be equal to the sampling frequency in the standard bone point coordinate library, or the sampling frequency of the real-time acquisition may be greater than the sampling frequency in the standard bone point coordinate library. When the sampling frequency acquired in real time can be greater than the sampling frequency in the standard bone point coordinate base, a plurality of data of the vector in the same time range are matched and calculated with one data in the vector formed by the bone points in the standard bone point coordinate base.

Step S130: and taking the collected two-dimensional skeleton motion model as a motion to be detected, dividing the motion to be detected into at least one motion stage to be detected according to the time of the target motion stage of the target motion, and forming a matching group by the target motion stage and the motion stage to be detected with corresponding time.

Specifically, for example, the target action is a deep squat, and is divided into two target action stages: squat and rise, the squat time being 2 seconds and the rise time being 2 seconds. According to time, correspondingly dividing the action to be tested into two action stages to be tested: squat down and rise up. And forming a matching group by the target action stage corresponding to squatting and the action stage to be tested, and forming a matching group by the target action stage corresponding to rising and the action stage to be tested.

Step S140: in each matching group, the action stage to be tested is divided into corresponding action of the part to be tested according to the action of the target part in the target action stage, and the action of the part to be tested in the action stage to be tested and the action of the target part in the corresponding target action stage form a part matching group.

For example, the motion phase to be measured is divided into five motion parts of a left arm, a right arm, a left leg, a right leg and a trunk. If the left arm, the right arm, the trunk and a random part of the target action stage are provided with identification items, taking the action of the part to be detected of the left arm and the action of the target part as a part matching group; taking the motion of the part to be measured of the right arm and the motion of the target part as a part matching group; taking the motion of the part to be detected of the trunk and the motion of the target part as a part matching group; the motion of the part to be measured of the random part and the motion of the target part are used as a part matching group.

Step S150: and for each part matching group, at least acquiring an identification item of random part action in target part action, acquiring a vector formed by at least two selected bone points in the action of the part to be detected according to the two-dimensional bone action model, and performing matching calculation on the vector of the random part action and a standard vector formed by corresponding standard coordinates in a standard bone point coordinate library so as to compare the vector with a vector threshold set by the identification parameters to acquire action identification feedback. I.e. identification and error correction according to the content of the different identification items as described in step S110 above.

In one embodiment, each of the fitness videos has a video file, the video file includes a number of a target action in the fitness video and a playing time of the target action, and the step S110 further includes: when the target action is played, searching a standard action database for a target action file of the number of the target action, wherein the target action file and the number of the target action are stored in the standard action database in a correlation manner, and each target action file comprises a target action stage of the target action, a target part action and an identification item corresponding to the target part action.

Step S160: and integrating the action recognition feedback of at least one matching group to obtain the action recognition feedback of the action to be detected.

In some embodiments, the target action at least includes a plurality of target action phases with a sequence, and in step S160, when the action recognition feedback of the previous target action phase and the corresponding action phase to be tested is that the action is not achieved, the action recognition feedback of the action achieved by the subsequent target action phase and the corresponding action phase to be tested is invalid.

In addition, the present invention does not limit the execution sequence of each step, for example, the step S110 and the step S120 are performed simultaneously, and the step S130 and the step S140 are performed simultaneously, which will not be described herein.

In an exemplary embodiment of the present disclosure, a computer-readable storage medium is further provided, on which a computer program is stored, which when executed by, for example, a processor, may implement the steps of the motion recognition error correction method described in any one of the above embodiments. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention described in the above-mentioned motion recognition error correction method section of the present description, when said program product is run on the terminal device.

Referring to fig. 11, a program product 300 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + +, C #, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the tenant computing device, partly on the tenant device, as a stand-alone software package, partly on the tenant computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing devices may be connected to the tenant computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

Engineering programs for performing the operations of the present invention may be built in any combination of one or more programming Integrated Development Environments (IDE), game development engines, such as Unity3D, Unreal, Visual Studio, and the like.

In an exemplary embodiment of the present disclosure, there is also provided an electronic device, which may include a processor, and a memory for storing executable instructions of the processor. Wherein the processor is configured to perform the steps of the motion recognition error correction method in any of the above embodiments via execution of the executable instructions.

As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 12. The electronic device 600 shown in fig. 12 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.

As shown in fig. 12, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 that connects the various system components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.

Wherein the storage unit stores program code executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present invention described in the above-mentioned motion recognition error correction method section of the present specification. For example, the processing unit 610 may perform the steps as described in fig. 1.

The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.

The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 630 may be one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a tenant to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above-mentioned motion recognition error correction method according to the embodiments of the present disclosure.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A motion recognition error correction method is characterized by comprising the following steps:

f. and integrating the action recognition feedback of at least one matching group to obtain the action recognition feedback of the action to be detected.

2. The motion recognition error correction method according to claim 1, wherein the matching calculation of the vector of the random part motion and a standard vector formed by corresponding standard coordinates in a standard bone point coordinate library to be compared with the vector threshold set by the recognition parameter comprises:

Vector of motion of random part

Cosine of angle θ between:

vector quantity

And vector

3. The motion recognition error correction method according to claim 1, wherein the matching calculation of the vector of the random part motion and a standard vector formed by corresponding standard coordinates in a standard bone point coordinate library to be compared with the vector threshold set by the recognition parameter further comprises:

4. The motion recognition error correction method according to claim 1, wherein for the process-oriented recognition item corresponding to the random part motion:

5. The motion recognition error correction method according to claim 1, wherein the random part motion further corresponds to one or more distance-oriented recognition items for which:

the identification parameter sets a distance threshold; and

6. The motion recognition error correction method according to claim 1, wherein the target motion at least includes a plurality of target motion phases having a sequential order, and the step f includes:

7. The motion recognition error correction method according to any one of claims 1 to 6, wherein each of the body parts includes three skeletal points and three vectors, the body part motion corresponds to one or more process-oriented or displacement-oriented recognition items, each recognition item includes a recognition object, a recognition parameter, and a recognition rule, and the recognition object includes at least one skeletal point of the three skeletal points; at least one of the three vectors; and one or more of an angle between two of the three vectors, the process-oriented identification term also comprising the library of standard bone point coordinates.

8. The motion recognition error correction method according to any one of claims 1 to 6, wherein during the motion of the motion, the sampling frequency of the motion of the part to be measured is equal to the sampling frequency of the standard skeleton point coordinate library.

9. The motion recognition error correction method according to any one of claims 1 to 6,

10. An electronic device, characterized in that the electronic device comprises:

a processor;

a storage medium having stored thereon a computer program which, when executed by the processor, performs the steps of any of claims 1 to 9.

11. A storage medium, having stored thereon a computer program which, when executed by a processor, performs the steps of any of claims 1 to 9.