CN114241607A - Personnel swivel chair detection method and system thereof - Google Patents
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Abstract
The invention discloses a personnel swivel chair detection method and a system thereof, comprising the following steps: acquiring a detection image; acquiring a pre-trained neural network model, and inputting the detection images into the neural network model frame by frame to generate human body detection frame information and coordinate information of human body key points; filtering the x-axis data of the coordinate information to generate filtered x-axis data, and generating a moving curve based on the filtered x-axis data and time information of an image frame corresponding to the filtered x-axis data; carrying out normalization processing on the filtered x-axis data and constructing a standard cosine curve; and performing similarity calculation based on the moving curve and the standard cosine curve and generating a detection result.
Description
Technical Field
The invention relates to the technical field of image detection, in particular to a personnel swivel chair detection method and a personnel swivel chair detection system.
Background
At present, for the external service posts (such as banks, public institutions, insurance companies and the like) of most enterprises, the regulations of the behavior of the ceremony instrument exist, such as the fact that the staff is not allowed to sit on a swivel chair to rotate, and the like, and therefore, the real-time detection of the behavior of the staff is needed.
However, the conventional deep learning network is identified based on the action identification model and is limited by the influence of insufficient materials, insufficient generalization, different practical application scenes and other factors of the training set, so that the action identification accuracy or the reliability of the deep learning network is low.
In conclusion, the existing personnel swivel chair detection method has the problem of low accuracy.
Disclosure of Invention
In view of this, the invention provides a method and a system for detecting a swivel chair, which solve the problem of low accuracy in the conventional method for detecting a swivel chair by improving a data extraction and processing method of the swivel chair.
In order to solve the problems, the technical scheme of the invention is to adopt a personnel revolving chair detection method, which comprises the following steps: acquiring a detection image; acquiring a pre-trained neural network model, and inputting the detection images into the neural network model frame by frame to generate human body detection frame information and coordinate information of human body key points; filtering the x-axis data of the coordinate information to generate filtered x-axis data, and generating a moving curve based on the filtered x-axis data and time information of an image frame corresponding to the filtered x-axis data; carrying out normalization processing on the filtered x-axis data and constructing a standard cosine curve; and performing similarity calculation based on the moving curve and the standard cosine curve and generating a detection result.
Optionally, inputting the detection image frame by frame into the neural network model to generate coordinate information of the human body key point, including: predicting and generating human body detection frame information and key point coordinates in the detection image based on the neural network model; updating the coordinates of the lower left corner of the detection frame contained in the human body detection frame information to the origin of a coordinate system, and establishing a human body target frame coordinate system; carrying out affine transformation on the updated human body detection frame information (x, y, w, h) and a preset rectangular region to generate an affine matrix; and generating coordinate information of the human body key points by the key point coordinates through the affine matrix.
Optionally, a filtering method used for filtering the x-axis data of the coordinate information is kalman filtering.
Optionally, the normalizing the filtered x-axis data and constructing a standard cosine curve includes: extracting unit image acquisition period of the filtered x-axis data; extracting peaks and troughs of the filtered x-axis data in a unit image acquisition period; and generating the standard cosine curve based on the unit image acquisition period and the wave crests and wave troughs.
Optionally, training the neural network model comprises: constructing an initialization network model, wherein the network model comprises a semantic segmentation model; acquiring a training data set and a testing data set which are formed by image samples containing multi-class marks, wherein the mark classes comprise human body detection frames and human body key points; training and testing the neural network model based on the training dataset and the testing dataset.
Optionally, performing similarity calculation based on the moving curve and the standard cosine curve and generating a detection result, including: performing Pearson similarity calculation based on the movement curve and the standard cosine curve and generating a similarity value; and if the similarity value is not higher than the preset threshold value, generating a detection result of the non-revolving chair.
Optionally, the human body key point is any one point of a human body double shoulder point, a human body left shoulder point and a human body right shoulder point.
Accordingly, the present invention provides a person swivel chair detection system, comprising: the camera shooting unit is used for collecting a detection image; the data processing unit is used for acquiring a pre-trained neural network model, inputting the detection image into the neural network model frame by frame to generate human body detection frame information and coordinate information of human body key points, filtering x-axis data of the coordinate information to generate filtered x-axis data, generating a moving curve based on the filtered x-axis data and time information of an image frame corresponding to the filtered x-axis data, performing normalization processing on the filtered x-axis data and constructing a standard cosine curve, and performing similarity calculation based on the moving curve and the standard cosine curve to generate a detection result.
Optionally, the data processing unit further includes a cache module, configured to store the detection image, the human body detection frame information, the coordinate information of the human body key point, and the swivel chair detection result.
The invention has the primary improvement that the provided swivel chair detection method utilizes the characteristic that the shoulders of a person regularly sway left and right in the swivel chair process, extracts human body detection frame information and key point coordinates through a neural network, reconstructs a human body target frame coordinate system based on the human body detection frame information and generates an affine matrix, further generates coordinate information of the human body key points based on the affine matrix and the key point coordinates, and avoids the influence of the change of the human body key point coordinates caused by the position movement of the human body. Meanwhile, similarity calculation is carried out by constructing the actually measured moving curve and the standard cosine curve generated by the theoretical revolving chair behavior, so that whether personnel are transferred or not is judged, and the problem of low accuracy in the traditional personnel revolving chair detection method is effectively solved.
Drawings
FIG. 1 is a simplified flow diagram of a person swivel detection method of the present invention;
fig. 2 is a simplified unit connection diagram of the person swivel detection system of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a method for detecting a revolving chair for people includes: acquiring a detection image; acquiring a pre-trained neural network model, and inputting the detection images into the neural network model frame by frame to generate human body detection frame information and coordinate information of human body key points; filtering the x-axis data of the coordinate information to generate filtered x-axis data, and generating a moving curve based on the filtered x-axis data and time information of an image frame corresponding to the filtered x-axis data; carrying out normalization processing on the filtered x-axis data and constructing a standard cosine curve; and performing similarity calculation based on the moving curve and the standard cosine curve and generating a detection result. Filtering the x-axis data of the coordinate information by using a Kalman filtering method; the key points of the human body are any one point of human body double shoulder points, human body left shoulder points and human body right shoulder points.
Further, inputting the detection image frame by frame into the neural network model to generate coordinate information of the human body key points, including: predicting and generating human body detection frame information and key point coordinates in the detection image based on the neural network model; updating the coordinates of the lower left corner of the detection frame contained in the human body detection frame information to the origin of a coordinate system, and establishing a human body target frame coordinate system; carrying out affine transformation on the updated human body detection frame information (x, y, w, h) and a preset rectangular region to generate an affine matrix; and generating coordinate information of the human body key points by the key point coordinates through the affine matrix. The size of the preset rectangular area can be defined by a user according to the detection precision of the camera, the actual monitoring requirement and the distance between the camera and a detected person, and the numerical value of the preset rectangular area is not specifically limited in the application.
Furthermore, to facilitate understanding of the working principle of generating coordinate information of key points of a human body, an example is given: in the 1920-1080 detected image, the position of a human body target frame is x:200, y:100, w:80 and h120, one key point of the shoulder is x:220, and y:110, after the coordinate system of the human body target frame is converted, the coordinate of the key point is x:20 and y:10, the human body detection frame is changed into x:0, y:0, w:80 and h120, and affine transformation is carried out on the translated position of the human body detection frame, namely x:0, y:0, w:80 and h120, and a preset rectangular area { (0,0) (200 ) (0,200) }, so that an affine matrix is obtained. Therefore, the coordinate information of the key points of the human body is generated by the key points x:20 and y:10 of the shoulders through an affine matrix, and the influence of the coordinate change of the key points of the human body caused by the position movement of the human body is avoided.
Further, the normalizing the filtered x-axis data and constructing a standard cosine curve includes: extracting unit image acquisition period of the filtered x-axis data; extracting peaks and troughs of the filtered x-axis data in a unit image acquisition period; and generating the standard cosine curve based on the unit image acquisition period and the wave crests and wave troughs.
Further, training the neural network model includes: constructing an initialization network model, wherein the network model comprises a semantic segmentation model; acquiring a training data set and a testing data set which are formed by image samples containing multi-class marks, wherein the mark classes comprise human body detection frames and human body key points; training and testing the neural network model based on the training dataset and the testing dataset. It should be noted that the neural network model used in the present application is conventional in the art, and does not relate to further improvement of the model architecture, and therefore, the type and architecture of the neural network model are not specifically limited. The type of the neural network model can be YOLO-V3, FASTER RCNN, and the like.
Further, performing similarity calculation based on the motion curve and the standard cosine curve and generating a detection result, including: performing Pearson similarity calculation based on the movement curve and the standard cosine curve and generating a similarity value; and if the similarity value is not higher than the preset threshold value, generating a detection result of the non-revolving chair. The preset threshold value can be defined by a user according to the detection precision of the camera, the actual monitoring requirement and the distance between the camera and a detected person, and the numerical value of the preset threshold value is not specifically limited in the application.
Further, generating a motion curve based on the filtered x-axis data and the time information of the image frame corresponding to the filtered x-axis data comprises: determining acquisition time information of the image frame corresponding to the filtering x-axis data based on the filtering x-axis data; and constructing a moving curve by taking the acquisition time as an x-axis and the value of the filtered x-axis data as a value of a y-axis.
According to the invention, the characteristic that the shoulders of a person regularly shake left and right in the process of rotating the chair is utilized, after the human body detection frame information and the key point coordinates are extracted through the neural network, the human body target frame coordinate system is reconstructed based on the human body detection frame information and an affine matrix is generated, and then the coordinate information of the human body key points is generated based on the affine matrix and the key point coordinates, so that the influence of the change of the human body key point coordinates caused by the position movement of the human body is avoided. Meanwhile, similarity calculation is carried out by constructing the actually measured moving curve and the standard cosine curve generated by the theoretical revolving chair behavior, so that whether personnel are transferred or not is judged, and the problem of low accuracy in the traditional personnel revolving chair detection method is effectively solved.
Accordingly, as shown in fig. 2, the present invention provides a system for detecting a revolving chair for people, comprising: the camera shooting unit is used for collecting a detection image; the data processing unit is used for acquiring a pre-trained neural network model, inputting the detection image into the neural network model frame by frame to generate human body detection frame information and coordinate information of human body key points, filtering x-axis data of the coordinate information to generate filtered x-axis data, generating a moving curve based on the filtered x-axis data and time information of an image frame corresponding to the filtered x-axis data, performing normalization processing on the filtered x-axis data and constructing a standard cosine curve, and performing similarity calculation based on the moving curve and the standard cosine curve to generate a detection result.
Further, the data processing unit extracts a unit image acquisition period of the filtered x-axis data; extracting peaks and troughs of the filtered x-axis data in a unit image acquisition period; and generating the standard cosine curve based on the unit image acquisition period and the wave crests and wave troughs.
Further, the data processing unit generates human body detection frame information and key point coordinates in the detection image based on the neural network model prediction; updating the coordinates of the lower left corner of the detection frame contained in the human body detection frame information to the origin of a coordinate system, and establishing a human body target frame coordinate system; carrying out affine transformation on the updated human body detection frame information (x, y, w, h) and a preset rectangular region to generate an affine matrix; and generating coordinate information of the human body key points by the key point coordinates through the affine matrix.
Furthermore, the data processing unit further comprises a cache module for storing the detection image, the human body detection frame information, the coordinate information of the human body key point and the detection result of the swivel chair.
The personnel swivel chair detection method and the system thereof provided by the embodiment of the invention are described in detail above. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Claims (9)
1. A personnel swivel chair detection method is characterized by comprising the following steps:
acquiring a detection image;
acquiring a pre-trained neural network model, and inputting the detection images into the neural network model frame by frame to generate human body detection frame information and coordinate information of human body key points;
filtering the x-axis data of the coordinate information to generate filtered x-axis data, and generating a moving curve based on the filtered x-axis data and time information of an image frame corresponding to the filtered x-axis data;
carrying out normalization processing on the filtered x-axis data and constructing a standard cosine curve;
and performing similarity calculation based on the moving curve and the standard cosine curve and generating a detection result.
2. The method for detecting the swivel chair according to claim 1, wherein inputting the detection image into the neural network model frame by frame to generate coordinate information of key points of a human body comprises:
predicting and generating human body detection frame information and key point coordinates in the detection image based on the neural network model;
updating the coordinates of the lower left corner of the detection frame contained in the human body detection frame information to the origin of a coordinate system, and establishing a human body target frame coordinate system;
carrying out affine transformation on the updated human body detection frame information (x, y, w, h) and a preset rectangular region to generate an affine matrix;
and generating coordinate information of the human body key points by the key point coordinates through the affine matrix.
3. The method of claim 1, wherein the filtering method used for filtering the x-axis data of the coordinate information is kalman filtering.
4. The method of claim 1, wherein normalizing the filtered x-axis data and constructing a standard cosine curve comprises:
extracting unit image acquisition period of the filtered x-axis data;
extracting peaks and troughs of the filtered x-axis data in a unit image acquisition period;
and generating the standard cosine curve based on the unit image acquisition period and the wave crests and wave troughs.
5. The method of claim 1, wherein training the neural network model comprises:
constructing an initialization network model, wherein the network model comprises a semantic segmentation model;
acquiring a training data set and a testing data set which are formed by image samples containing multi-class marks, wherein the mark classes comprise human body detection frames and human body key points;
training and testing the neural network model based on the training dataset and the testing dataset.
6. The method for detecting the swivel chair according to claim 1, wherein performing similarity calculation based on the movement curve and the standard cosine curve and generating a detection result comprises:
performing Pearson similarity calculation based on the movement curve and the standard cosine curve and generating a similarity value;
and if the similarity value is not higher than the preset threshold value, generating a detection result of the non-revolving chair.
7. The method for detecting the swivel chair according to claim 5, wherein the human body key point is any one point of a human body double shoulder point, a human body left shoulder point and a human body right shoulder point.
8. A person swivel chair detection system, comprising:
the camera shooting unit is used for collecting a detection image;
the data processing unit is used for acquiring a pre-trained neural network model, inputting the detection image into the neural network model frame by frame to generate human body detection frame information and coordinate information of human body key points, filtering x-axis data of the coordinate information to generate filtered x-axis data, generating a moving curve based on the filtered x-axis data and time information of an image frame corresponding to the filtered x-axis data, performing normalization processing on the filtered x-axis data and constructing a standard cosine curve, and performing similarity calculation based on the moving curve and the standard cosine curve to generate a detection result.
9. The system of claim 8, wherein the data processing unit further comprises a buffer module for storing the detection image, the human detection frame information, the coordinate information of the human key point, and the detection result of the swivel chair.
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