CN113723187A - Semi-automatic labeling method and system for gesture key points - Google Patents
Semi-automatic labeling method and system for gesture key points Download PDFInfo
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Abstract
The embodiment of the invention provides a semi-automatic labeling method and a semi-automatic labeling system for gesture key points, wherein the method comprises the following steps: inputting the picture to be marked into a hand detection model and a hand key point detection model which are trained in advance, and obtaining output hand coordinates and hand key point coordinates; and taking the hand coordinates and the hand key point coordinates as pre-labeling data. According to the embodiment of the invention, the gesture key points are labeled in advance through deep learning, then fine adjustment is carried out manually, and the labeled data set can train the deep learning model again, so that a closed loop is formed, and the robustness and the labeling efficiency of the model are improved.
Description
Technical Field
The invention relates to the field of rail transit, in particular to a semi-automatic labeling method and system for a gesture key point.
Background
In recent years, with the rapid development of technologies such as artificial intelligence and 5G networks, Human Computer Interaction (Human Computer Interaction) has become one of the important scientific technologies. Gesture input is an important non-contact human-computer interaction mode, and is often used in smart homes, smart wearing, virtual reality, augmented reality and other scenes. At present, gesture recognition methods are divided into external hardware equipment based, computer vision based and deep learning based, wherein the gesture recognition based on the external hardware equipment acquires hand motion information through a data glove, although the recognition rate is high, the hardware equipment is generally expensive and is easy to sweat after being worn for a long time; the gesture recognition based on computer vision adopts features such as skin color, texture and the like to extract key points, but is easily influenced by shielding and illumination, so that the accuracy is low and the robustness is poor; the gesture recognition based on deep learning adopts a supervised learning mode, firstly training and learning are carried out from a labeled data set, and then the gesture in the picture is detected and recognized. At present, deep learning is greatly developed in the fields of image processing, computer vision, target detection and the like, the link of manually selecting characteristics in the traditional method is avoided, an end-to-end mode is adopted for directly learning from data set, the mode avoids the influence of human factors, and the method is higher in precision and better in robustness.
Gesture recognition methods based on deep learning require a large number of labeled datasets. The data sets are often acquired by manual labeling, and the method is low in efficiency, so that a large amount of labor and time cost is consumed, and therefore, the improvement of the labeling efficiency is an urgent problem to be solved.
Disclosure of Invention
In order to solve the above problems, embodiments of the present invention provide a semi-automatic labeling method and system for gesture key points, which overcome the above problems or at least partially solve the above problems.
According to a first aspect of the embodiments of the present invention, a method for semi-automatically labeling a gesture key point is provided, the method including: inputting the picture to be marked into a hand detection model and a hand key point detection model which are trained in advance, and obtaining output hand coordinates and hand key point coordinates; and taking the hand coordinates and the hand key point coordinates as pre-labeling data.
According to a second aspect of the embodiments of the present invention, there is provided a system for semi-automatically labeling a gesture key point, the system including: the input module is used for inputting the pictures to be marked into a hand detection model and a hand key point detection model which are trained in advance to obtain output hand coordinates and hand key point coordinates; and the marking module is used for taking the hand coordinates and the hand key point coordinates as pre-marking data.
According to a third aspect of the embodiments of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement a semi-automatic labeling method for a gesture keypoint as provided in any one of various possible implementations of the first aspect.
According to a fourth aspect of embodiments of the present invention, there is provided a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a semi-automatic labeling method for gesture keypoints as provided in any one of the various possible implementations of the first aspect.
According to the semi-automatic labeling method and system for the gesture key points, the gesture key points are labeled in advance through deep learning, then fine tuning is carried out manually, and the labeled data set can train the deep learning model again, so that a closed loop is formed, and the robustness of the model and the labeling efficiency are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from these without inventive effort.
Fig. 1 is a schematic flowchart of a semi-automatic labeling method for gesture key points according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a semi-automatic labeling method for key points of gestures according to another embodiment of the present invention;
fig. 3 is a schematic structural diagram of a semi-automatic labeling system for gesture key points according to an embodiment of the present invention;
fig. 4 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments, but not all embodiments, of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to solve the technical problems in the prior art, the semi-automatic labeling method for the gesture key points provided by the embodiment of the invention firstly adopts a deep learning method to perform pre-labeling, and then manually fine-tunes the labeling area, so as to obtain a labeling result. Referring to fig. 1, an embodiment of the present invention provides a semi-automatic labeling method for a gesture key point, where the method includes, but is not limited to, the following steps:
Wherein, before step 101 is executed, the hand detection model and the hand key point detection model may be firstly subjected to model training.
Wherein, the model training process to the hand detection model specifically can adopt following mode: before picture input to hand detection model and the hand key point detection model that trains in advance will treat the mark, still include: and after data enhancement is carried out on the hand data set, inputting a hand detection model to carry out model training, and obtaining the trained hand detection model.
Specifically, the model type of the hand detection model may be the YOLO V4 model.
The model training process aiming at the hand key point detection model can specifically adopt the following mode: and after data enhancement is carried out on the hand key point data set, inputting a hand key point detection model for model training, and obtaining the trained hand key point detection model.
Specifically, the model type of the hand keypoint detection model may be the ResNet50 model, and the data set used for training may be a 21 keypoint detection data set.
For the training process of the two models, the data enhancement can be implemented as follows: picture size, up-down and left-right mirroring are adjusted to increase the number of data sets.
Specifically, data enhancement, including adjusting the picture size, up-down and left-right mirroring, changes the number of data sets to a certain multiple, for example, 5 times, to enrich the diversity of the data sets.
Aiming at the training process of the two models, the model training process can adopt the following mode: and adjusting learning rate, batch _ size and epoch parameters, observing the change condition of the loss value and the accuracy rate along with the parameters, and taking out the optimal value.
Therefore, the trained hand detection model and hand key point detection model can be obtained through the model training process, and then the actual labeling stage, that is, step 101, can be entered.
Specifically, in step 101, a hand detection model and a hand key point detection model (i.e., YOLO V4 and ResNet50 models) may be deployed in a server, and after a picture to be annotated is input into the model, rectangular frame coordinates (i.e., hand coordinates) of a hand and 21 hand key point coordinates (i.e., hand key point coordinates) are obtained.
And 102, taking the hand coordinates and the hand key point coordinates as pre-labeling data.
Based on the content of the foregoing embodiment, as an alternative embodiment, after step 102, the method further includes: and correcting the pre-marked data.
Specifically, in practical engineering, the error of the labeled data is within 3-5 pixels, so the correctness of the pre-labeled data needs to be checked (for example, manually checked) and corrected, so as to obtain labeled data with higher correctness.
Fig. 2 is a schematic flow chart of a semi-automatic labeling method for gesture key points according to another embodiment of the present invention, and referring to fig. 2, after step 102, based on the contents of the foregoing embodiment, as an optional embodiment, after step 102 uses the hand coordinates and the hand key point coordinates as pre-labeled data, the method further includes: and training the hand detection model and the hand key point detection model by adopting the pre-labeling data.
The pre-labeled data is a labeled data set, and the deep learning model can be trained again by using the labeled data set, so that a closed loop is formed, and the robustness and the labeling efficiency of the model are improved.
Based on the content of the foregoing embodiment, as an alternative embodiment, after step 102, the method may further include: and training the hand detection model and the hand key point detection model by adopting the corrected pre-labeled data. And the corrected pre-labeled data is the correct labeled data set.
Specifically, the correctly labeled data set obtained in the actual use stage is input into the model again and trained, so that the accuracy and robustness of the model can be improved, the precision of pre-labeling is improved, and the labor cost and the time cost are reduced.
According to the semi-automatic labeling method for the gesture key points, the gesture key points are labeled in advance through deep learning, then fine tuning is carried out manually, and the labeled data set can train the deep learning model again, so that a closed loop is formed, and the robustness of the model and the labeling efficiency are improved.
Based on the content of the above embodiment, the embodiment of the present invention provides a semi-automatic labeling system for gesture key points, where the semi-automatic labeling system for gesture key points is used to execute the semi-automatic labeling method for gesture key points in the above method embodiments. Referring to fig. 3, the system includes: the input module 301 is configured to input the picture to be labeled into a pre-trained hand detection model and a pre-trained hand key point detection model, and obtain output hand coordinates and hand key point coordinates; and the labeling module 302 is configured to use the hand coordinates and the hand key point coordinates as pre-labeling data.
An embodiment of the present invention provides an electronic device, as shown in fig. 4, the electronic device includes: a processor (processor)501, a communication Interface (Communications Interface)502, a memory (memory)503, and a communication bus 504, wherein the processor 501, the communication Interface 502, and the memory 503 are configured to communicate with each other via the communication bus 504. The processor 501 may call a computer program running on the memory 503 and on the processor 501 to execute the semi-automatic labeling method for the gesture key points provided by the above embodiments, for example, including: inputting the picture to be marked into a hand detection model and a hand key point detection model which are trained in advance, and obtaining output hand coordinates and hand key point coordinates; and taking the hand coordinates and the hand key point coordinates as pre-labeling data.
An embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the semi-automatic labeling method for a gesture key point provided in the foregoing embodiments when executed by a processor, for example, the method includes: inputting the picture to be marked into a hand detection model and a hand key point detection model which are trained in advance, and obtaining output hand coordinates and hand key point coordinates; and taking the hand coordinates and the hand key point coordinates as pre-labeling data.
It should be noted that, in the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to relevant descriptions of other embodiments for parts that are not described in detail in a certain embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A semi-automatic labeling method for gesture key points is characterized by comprising the following steps:
inputting the picture to be marked into a hand detection model and a hand key point detection model which are trained in advance, and obtaining output hand coordinates and hand key point coordinates;
and taking the hand coordinates and the hand key point coordinates as pre-labeling data.
2. The method of claim 1, wherein before inputting the picture to be labeled into the pre-trained hand detection model and hand key point detection model, the method further comprises:
and after data enhancement is carried out on the hand data set, inputting a hand detection model to carry out model training, and obtaining the trained hand detection model.
3. The method of claim 1, wherein before inputting the picture to be labeled into the pre-trained hand detection model and hand key point detection model, the method further comprises:
and after data enhancement is carried out on the hand key point data set, inputting a hand key point detection model for model training, and obtaining the trained hand key point detection model.
4. The method of claim 2 or 3, wherein the data enhancement comprises:
picture size, up-down and left-right mirroring are adjusted to increase the number of data sets.
5. The method of claim 2 or 3, wherein the model training comprises:
and adjusting learning rate, batch _ size and epoch parameters, observing the change condition of the loss value and the accuracy rate along with the parameters, and taking out the optimal value.
6. The method of claim 1, wherein after using the hand coordinates and the hand keypoint coordinates as pre-annotation data, further comprising:
and correcting the pre-marked data.
7. The method of claim 6, wherein after the modifying the pre-annotated data, further comprising:
training the hand detection model and the hand key point detection model by adopting the pre-marked data;
or, training the hand detection model and the hand key point detection model by adopting the corrected pre-labeled data.
8. A semi-automatic labeling system for gesture key points, comprising:
the input module is used for inputting the pictures to be marked into a hand detection model and a hand key point detection model which are trained in advance to obtain output hand coordinates and hand key point coordinates;
and the marking module is used for taking the hand coordinates and the hand key point coordinates as pre-marking data.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for semi-automatically labeling gesture keypoints according to any one of claims 1 to 7 when executing the program.
10. A non-transitory computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, performs the steps of a method for semi-automatic labeling of gesture keypoints according to any one of claims 1 to 7.
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