CN113792674A - Method and device for determining unoccupied seat rate and electronic equipment - Google Patents
Method and device for determining unoccupied seat rate and electronic equipment Download PDFInfo
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Abstract
The embodiment of the present specification provides a method, an apparatus, and an electronic device for determining an unoccupied seat rate, where in the method for determining an unoccupied seat rate, after acquiring an image in a target location acquired by a camera, a server detects the image, acquires a seat and a human body in the image, then determines a first pixel coordinate of the seat and a second pixel coordinate of the human body, determines a first coordinate in a ground plane coordinate system according to the first pixel coordinate, determines a second coordinate in the ground plane coordinate system according to the second pixel coordinate, and finally determines a current number of seats in the target location according to the first coordinate and determines a current number of users in the target location according to the second coordinate, thereby determining a current unoccupied seat rate of the target location, so as to determine a real-time unoccupied seat rate of the target location according to an image captured by the camera disposed in the target location, thereby optimizing the service efficiency of the target site.
Description
[ technical field ] A method for producing a semiconductor device
The embodiment of the specification relates to the technical field of internet, in particular to a method and a device for determining a vacant seat rate and electronic equipment.
[ background of the invention ]
In the current catering industry, a user generally does not know the detailed dining number of people and the peak time of passenger flow of nearby shops, can only roughly estimate the explosion degree of each shop through historical experience, and finally selects a satisfactory shop for dining by combining the current time arrangement and/or taste preference and the actual situation after the shop. Thus, the user is inevitably waiting for a long time after arriving at the store or changing the preferred store.
In order to solve the problem that a user waits for or changes a preferred store for a long time after arriving at the store, the real-time vacant seat rate and the passenger flow peak time of each store can be sorted according to the preference of the user, and the real-time vacant seat rate and the passenger flow peak time can be displayed to the user in a real-time map data mode. On one hand, the real-time vacant seat rate can help the user to select a suitable shop according to the self needs, and a digital optimal decision is formed before the user arrives at the shop, rather than a fuzzy decision based on historical experience; on the other hand, for the shop, the service efficiency can be optimized according to the real-time vacant seat rate, and the peak time length can be prolonged by combining with take-out and/or coupons and the like.
Therefore, it is desirable to provide a method for determining the vacancy rate to determine the real-time vacancy rate of the store.
[ summary of the invention ]
The embodiment of the specification provides a method and a device for determining an unoccupied seat rate and electronic equipment, so as to determine the real-time unoccupied seat rate of a target place according to an image shot by a camera arranged in the target place, and further optimize the service efficiency of the target place.
In a first aspect, an embodiment of the present specification provides a method for determining an unoccupied seat rate, which is applied to a server, and the method for determining the unoccupied seat rate includes: acquiring an image in a target place acquired by a camera; wherein the camera is arranged in the target site; detecting the image to obtain a seat and a human body in the image; determining a first pixel coordinate of a contact point of the seat and the ground, and determining a second pixel coordinate of a contact point of the foot of the human body and the ground; determining a first coordinate of the contact point of the seat and the ground in a ground plane coordinate system according to the first pixel coordinate, and determining a second coordinate of the contact point of the foot of the human body and the ground in the ground plane coordinate system according to the second pixel coordinate; determining the current seat number in the target place according to the first coordinate, and determining the current user number in the target place according to the second coordinate; and determining the current vacant seat rate of the target place according to the seat number and the number of users.
In the method for determining the unoccupied seat rate, after acquiring an image in a target place acquired by a camera, a server detects the image to acquire a seat and a human body in the image, then determines a first pixel coordinate of a contact point between the seat and the ground, determines a second pixel coordinate of a contact point between a foot of the human body and the ground, determines a first coordinate of the contact point between the seat and the ground in a ground plane coordinate system according to the first pixel coordinate, determines a second coordinate of the contact point between the foot of the human body and the ground in the ground plane coordinate system according to the second pixel coordinate, determines the current number of seats in the target place according to the first coordinate, determines the current number of users in the target place according to the second coordinate, determines the current unoccupied seat rate of the target place according to the number of seats and the number of users, and thereby realizing the image shot by the camera arranged in the target place, determining the real-time vacant seat rate of the target place, and further optimizing the service efficiency of the target place; in addition, the method utilizes the existing camera in the target place to shoot the image, a passenger flow meter does not need to be installed at the gate of the target place, the construction difficulty in the aspects of installation and power supply is reduced, and the applicability is high.
In one possible implementation manner, the determining, according to the first pixel coordinate, a first coordinate of the contact point between the seat and the ground in a ground plane coordinate system, and determining, according to the second pixel coordinate, a second coordinate of the contact point between the foot of the human body and the ground in the ground plane coordinate system includes: extracting coordinates of orthogonal vanishing points in the image by detecting straight line segments in the image; calculating the focal length and the external parameter matrix of the camera according to the coordinate of the orthogonal vanishing point; obtaining a change matrix of the image and the camera coordinate system according to the external reference matrix and the height of the camera from the ground; and performing three-dimensional semantic reconstruction according to the first pixel coordinate and the second pixel coordinate, the focal length of the camera and the change matrix to determine the first coordinate and the second coordinate.
In one possible implementation manner, the number of the cameras is at least two; after the image in the target place that obtains the camera collection still includes: extracting feature points from the images collected by the at least two cameras; matching the extracted feature points to obtain feature point matching pairs; and determining a transformation relation between the at least two cameras according to the feature point matching pair so as to splice the images acquired by the at least two cameras.
In one possible implementation manner, after determining, according to the first pixel coordinate, a first coordinate of the seat-to-ground contact point in a ground plane coordinate system, and determining, according to the second pixel coordinate, a second coordinate of the foot-to-ground contact point of the human body in the ground plane coordinate system, the method further includes: when at least two first coordinates are obtained in the area where the seat is located through detection, carrying out non-maximum suppression processing on the at least two first coordinates, and reserving the first coordinate with the highest built-in reliability in the area where the seat is located as the coordinate of the contact point of the seat and the ground in the ground plane coordinate system; and/or when at least two second coordinates are obtained in the region where the human body is located through detection, performing non-maximum suppression processing on the at least two second coordinates, and reserving the second coordinate with the highest confidence level in the region where the human body is located as the coordinate of the contact point of the foot of the human body and the ground in the ground plane coordinate system.
In one possible implementation manner, the first coordinate includes at least two coordinates of a first seat obtained by detecting the first seat in consecutive N frames of images before the current time; after determining the first coordinate of the seat-to-ground contact point in the ground plane coordinate system according to the first pixel coordinate, the method further includes: and carrying out non-maximum suppression processing on at least two coordinates of the first seat, and reserving the coordinate with the highest confidence degree in the at least two coordinates of the first seat as the coordinate of the contact point of the first seat and the ground in a ground plane coordinate system.
In a second aspect, an embodiment of the present specification provides an apparatus for determining a vacant seat rate, including: the acquisition module is used for acquiring images in a target place acquired by the camera; wherein the camera is arranged in the target site; the detection module is used for detecting the image and acquiring a seat and a human body in the image; the coordinate determination module is used for determining first pixel coordinates of a contact point of the seat and the ground and determining second pixel coordinates of a contact point of the foot of the human body and the ground; determining a first coordinate of the contact point of the seat and the ground in a ground plane coordinate system according to the first pixel coordinate, and determining a second coordinate of the contact point of the foot of the human body and the ground in the ground plane coordinate system according to the second pixel coordinate; the unoccupied seat rate determining module is used for determining the current seat number in the target place according to the first coordinate and determining the current user number in the target place according to the second coordinate; and determining the current vacant seat rate of the target place according to the seat number and the number of users.
In one possible implementation manner, the coordinate determination module includes: the coordinate extraction submodule is used for extracting the coordinates of the orthogonal vanishing points in the image by detecting straight line segments in the image; the calculation submodule is used for calculating the focal length and the external parameter matrix of the camera according to the coordinate of the orthogonal vanishing point; obtaining a change matrix of the image and the camera coordinate system according to the external parameter matrix and the height of the camera from the ground; and the reconstruction submodule is used for performing three-dimensional semantic reconstruction according to the first pixel coordinate, the second pixel coordinate, the focal length of the camera and the change matrix so as to determine the first coordinate and the second coordinate.
In one possible implementation manner, the apparatus further includes: the extraction module is used for extracting feature points from the images collected by the at least two cameras after the acquisition module acquires the images in the target place collected by the cameras when the number of the cameras is at least two; the matching module is used for matching the characteristic points extracted by the extraction module to obtain characteristic point matching pairs; and the scene splicing module is used for determining the transformation relation between the at least two cameras according to the feature point matching pairs so as to splice the scenes of the images acquired by the at least two cameras.
In one possible implementation manner, the apparatus further includes: the reservation module is used for carrying out non-maximum suppression processing on at least two first coordinates when the at least two first coordinates are obtained by detection in the area where the seat is located, and reserving the first coordinate with the highest built-in reliability in the area where the seat is located as the coordinate of the contact point between the seat and the ground in the ground plane coordinate system; and/or when at least two second coordinates are obtained in the region where the human body is located through detection, performing non-maximum suppression processing on the at least two second coordinates, and reserving the second coordinate with the highest confidence level in the region where the human body is located as the coordinate of the contact point of the foot of the human body and the ground in the ground plane coordinate system.
In one possible implementation manner, the first coordinate includes at least two coordinates of a first seat obtained by detecting the first seat in consecutive N frames of images before the current time; the device further comprises: and the retaining module is used for performing non-maximum suppression processing on at least two coordinates of the first seat after the coordinate determination module determines the first coordinate of the contact point between the seat and the ground in the ground plane coordinate system according to the first pixel coordinate, and retaining the coordinate with the highest confidence among the at least two coordinates of the first seat as the coordinate of the contact point between the first seat and the ground in the ground plane coordinate system.
In a third aspect, an embodiment of the present specification provides an electronic device, including: at least one processor; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, the processor calling the program instructions to be able to perform the method provided by the first aspect.
In a fourth aspect, embodiments of the present specification provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method provided in the first aspect.
It should be understood that the second to fourth aspects of the embodiments of the present description are consistent with the technical solution of the first aspect of the embodiments of the present description, and similar beneficial effects are obtained in all aspects and corresponding possible implementation manners, and are not described again.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a method for determining a vacant seat rate according to an embodiment of the present disclosure;
FIG. 2 is a schematic illustration of an image within a target site provided by one embodiment of the present description;
FIG. 3 is a schematic diagram of detecting an image of a seat and a person provided in one embodiment of the present description;
fig. 4 is a flowchart of a method for determining a vacant seat rate according to another embodiment of the present disclosure;
FIG. 5 is a schematic diagram of image processing provided in one embodiment of the present description;
FIG. 6 is a flow chart of a method for determining a vacant seat rate according to yet another embodiment of the present disclosure;
FIG. 7 is a flow chart of a method for determining a vacant seat rate according to yet another embodiment of the present disclosure;
FIG. 8 is a schematic diagram of image processing provided in another embodiment of the present description;
fig. 9 is a schematic structural diagram of a device for determining a vacant seat ratio according to an embodiment of the present disclosure;
fig. 10 is a schematic structural diagram of a device for determining a vacant seat ratio according to another embodiment of the present disclosure;
fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
[ detailed description ] embodiments
For better understanding of the technical solutions in the present specification, the following detailed description of the embodiments of the present specification is provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only a few embodiments of the present specification, and not all embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step are within the scope of the present specification.
The terminology used in the embodiments of the specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the specification. As used in the specification examples and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
In order to solve the problem that a user waits for or changes a preferred store for a long time after arriving at the store, the real-time vacant seat rate and the passenger flow peak time of each store can be sorted according to the preference of the user, and the real-time vacant seat rate and the passenger flow peak time can be displayed to the user in a real-time map data mode.
Two methods for counting the vacant seat rate are provided in the prior art:
1) seat code scanning: the number of current dinning people in the store is counted in real time in a code scanning and meal ordering mode, and the current in-store vacant seat rate is obtained by combining the total seat number counted manually in advance. In the scheme, the number of actual diners is input by service staff in a shop, the accuracy is higher by human factors, and the workload of the service staff is increased;
2) and (3) passenger flow counting: and (3) adopting a passenger flow meter such as an infrared scanner and/or a camera to carry out passenger flow volume statistics at the storefront door opening, and combining the total seat number counted manually in advance to obtain the indoor seat rate of the current store. The scheme is influenced by the problems of simultaneous entrance/exit of multiple persons, multiple entrances and exits, shielding and the like, the counting accuracy of the number of persons is limited, and the passenger flow meter is generally placed at a doorway and has higher requirements on equipment installation and/or power supply and the like.
The embodiment of the specification provides a method for determining a vacant seat rate, which is used for completing automatic calibration of a camera pose by detecting a vanishing point of an image shot by a camera (such as a monitoring camera), so that three-dimensional semantic reconstruction of a seat and a human body is realized. According to the indoor layout, non-maximum suppression and point aggregation are carried out on the seats and the human body, the seat detection and human body detection precision is improved, and the real-time seat vacancy rate precision is further improved.
According to the method for determining the unoccupied seat rate, the monitoring camera arranged in the target place can be used for shooting images, automatic calibration of the pose of the camera is achieved through Manhattan hypothesis according to the images, and then three-dimensional reconstruction is carried out on indoor semantic information by combining a depth learning detection algorithm and inverse perspective transformation, so that the unoccupied seat rate in the store is estimated, manual registration is not needed, a passenger flow meter is not needed to be installed at the door, and the method has a larger efficiency advantage and a deployment advantage compared with the existing scheme.
Fig. 1 is a flowchart of a method for determining an unoccupied seat rate according to an embodiment of the present disclosure, where the method for determining an unoccupied seat rate may be applied to a server, and as shown in fig. 1, the method for determining an unoccupied seat rate may include:
102, acquiring an image in a target place acquired by a camera; wherein the camera is arranged in the target place.
Specifically, the camera may be a monitoring camera disposed in the target location, so that the server may directly obtain an image collected by the monitoring camera in the target location, where the collected image may be as shown in fig. 2, and fig. 2 is a schematic diagram of an image in the target location provided in an embodiment of this specification.
And 104, detecting the image to acquire the seat and the human body in the image.
Specifically, the seats in the above-described image may be detected by a deep learning method, such as: a chair or a sofa, etc. The deep learning method for detecting the seat may include: YOLO, single shot multi-box detector (SSD), fast area convolutional neural network (fast RCNN), mask area convolutional neural network (mask RCNN), and/or the like.
Similarly, the human body in the image may also be detected by a deep learning method, wherein the deep learning method that may be used to detect the human body may include: YOLO, SSD, master RCNN, and/or mask RCNN, and the like.
In a specific implementation, taking fig. 2 as an example, after detecting the seat and the human body in the image shown in fig. 2, the image shown in fig. 3 may be obtained, and fig. 3 is a schematic diagram of detecting the seat and the human body provided in an embodiment of the present disclosure.
And 106, determining a first pixel coordinate of a contact point between the seat and the ground, and determining a second pixel coordinate of a contact point between the foot of the human body and the ground.
And step 108, determining a first coordinate of the contact point of the seat and the ground in a ground plane coordinate system according to the first pixel coordinate, and determining a second coordinate of the contact point of the foot of the human body and the ground in the ground plane coordinate system according to the second pixel coordinate.
Specifically, the server may determine a first coordinate of the contact point between the seat and the ground in the ground plane coordinate system through inverse perspective transformation according to the first pixel coordinate, and determine a second coordinate of the contact point between the foot of the human body and the ground in the ground plane coordinate system through inverse perspective transformation according to the second pixel coordinate.
And step 110, determining the current seat number in the target place according to the first coordinate, and determining the current user number in the target place according to the second coordinate.
And 112, determining the current vacant seat rate of the target place according to the seat number and the user number.
Specifically, the difference between the number of seats and the number of users may be calculated, where the difference is the number of currently empty seats in the target location, and then the quotient obtained by dividing the number of currently empty seats by the number of seats is calculated, where the quotient is the current empty seat rate of the target location.
In the method for determining the unoccupied seat rate, after acquiring an image in a target place acquired by a camera, a server detects the image to acquire a seat and a human body in the image, then determines a first pixel coordinate of a contact point between the seat and the ground, determines a second pixel coordinate of a contact point between a foot of the human body and the ground, determines a first coordinate of the contact point between the seat and the ground in a ground plane coordinate system according to the first pixel coordinate, determines a second coordinate of the contact point between the foot of the human body and the ground in the ground plane coordinate system according to the second pixel coordinate, determines the current number of seats in the target place according to the first coordinate, determines the current number of users in the target place according to the second coordinate, determines the current unoccupied seat rate of the target place according to the number of seats and the number of users, and thereby realizing the image shot by the camera arranged in the target place, determining the real-time vacant seat rate of the target place, and further optimizing the service efficiency of the target place; in addition, the method utilizes the existing camera in the target place to shoot the image, a passenger flow meter does not need to be installed at the gate of the target place, the construction difficulty in the aspects of installation and power supply is reduced, and the applicability is high.
Fig. 4 is a flowchart of a method for determining a vacant seat ratio according to another embodiment of the present disclosure, and as shown in fig. 4, in the embodiment shown in fig. 1 of the present disclosure, step 108 may include:
And step 404, calculating the focal length and the external parameter matrix of the camera according to the coordinates of the orthogonal vanishing points.
Specifically, three orthogonal vanishing point coordinates (u) on the image may be extracted by detecting a straight line segment on the image1v1)、(u2v2)、(u3v3) And resolving the focal length and the external parameter matrix of the camera according to the vanishing point coordinates.
The calculation method of the camera focal length f may be as shown in formula (1):
the calculation process of the external reference matrix R can be represented by the following formulas (2) and (3):
R＝R0/‖R0‖ (3)
in the formulae (1) to (3), (u)0 v0) The pixel coordinates of the center point of the image are half the width and half the height of the image, respectively.
And 406, obtaining a change matrix of the image and the camera coordinate system according to the external reference matrix and the height of the camera from the ground.
Specifically, according to the external reference matrix obtained in step 404 and the previously obtained height h (typically, 2.5m to 3.5m in a room) of the camera from the ground, a change matrix [ R | t ] of the image and the camera coordinate system can be obtained, as shown in formula (4).
And step 408, performing three-dimensional semantic reconstruction according to the first pixel coordinate and the second pixel coordinate, the focal length of the camera and the change matrix to determine the first coordinate and the second coordinate.
Specifically, after the focal length of the camera and the change matrix are obtained, the three-dimensional semantic reconstruction can be performed by combining the first pixel coordinate and the second pixel coordinate.
For example, assume that the pixel coordinate (first pixel coordinate or second pixel coordinate) of the detection object is (u v), and its ground plane coordinate is PW＝[xW yW 0]TThe superscript W represents the horizon coordinate system; the computation process of the three-dimensional semantic reconstruction can be as shown in equation (5).
In equation (5), (u v) is the pixel coordinates of the detected object in the image; pCRepresenting three-dimensional coordinates under a camera coordinate system; s represents a scale factor.
The present embodiment describes processes of camera self-calibration and inverse perspective transformation, and schematic diagrams of image processing of the above two processes may be as shown in fig. 5, where fig. 5 is a schematic diagram of image processing provided in an embodiment of this specification.
Fig. 6 is a flowchart of a method for determining a vacant seat rate according to yet another embodiment of the present disclosure, where in this embodiment, the number of the cameras may be at least two; thus, as shown in fig. 6, in the embodiment shown in fig. 1 of this specification, after step 102, the method may further include:
And step 604, matching the extracted feature points to obtain feature point matching pairs.
And 606, determining a transformation relation between the at least two cameras according to the feature point matching pairs so as to splice the images acquired by the at least two cameras.
Specifically, the feature points may be scale-invariant feature transform (SIFT) feature points, the SIFT feature points are extracted from the images collected by the at least two cameras and matched to obtain a plurality of sets of two-dimensional (2D) feature point matching pairs, and a transformation relationship between the at least two cameras is solved from the plurality of sets of 2D feature point matching pairs through epipolar constraint, so as to implement scene splicing of multiple images.
Fig. 7 is a flowchart of a method for determining a free seat rate according to still another embodiment of the present disclosure, as shown in fig. 7, in the embodiment shown in fig. 1 of the present disclosure, after step 108, the method may further include:
That is to say, in a specific implementation, since there may be false detection in the detection algorithms of the seat and the human body, a plurality of seats or human bodies are stacked together, and only one of the detection targets with the highest confidence level in the range is reserved for the same type of detection target by performing non-maximum suppression on the reconstructed three-dimensional coordinates of each detection target, the image processing schematic diagram may be as shown in fig. 8, where fig. 8 is a schematic diagram of image processing provided in another embodiment of this specification.
Wherein, non-maximum suppression: soft-NMS (non-maximum rendering), an algorithm for removing non-maxima, is commonly used in detection and/or identification algorithms in computer vision.
In addition, in the embodiment shown in fig. 1 of the present specification, the first coordinate may include at least two coordinates of a first seat obtained by detecting the first seat in N consecutive images before the current time; in this way, after the first coordinate of the contact point between the seat and the ground in the ground plane coordinate system is determined based on the first pixel coordinate, the server may perform non-maximum suppression processing on at least two coordinates of the first seat, and retain a coordinate with the highest degree of reliability among the at least two coordinates of the first seat as the coordinate of the contact point between the first seat and the ground in the ground plane coordinate system. Where N >1, N is a positive integer, and the size of N is not limited in this embodiment, for example, N may be 10.
That is to say, in the specific implementation, since there may be missing detection in the detection of the seat, it is necessary to aggregate the detection results of the same point in the continuous multi-frame images, and specifically, the method may be: and reserving all the detected targets appearing in the latest 10 frames, and if the same target appears repeatedly for multiple times, performing non-maximum suppression processing on the coordinates of the target in the latest 10 frames and reserving the coordinates with the highest confidence.
In the method for determining the unoccupied seat rate provided by the embodiment of the specification, firstly, a deep learning algorithm is used for detecting the seat and the human body in an image, then, three-dimensional reconstruction of the seat and the human body is realized through camera self-calibration, inverse perspective transformation and scene splicing, and finally, the accuracy of the unoccupied seat rate is improved through a post-processing algorithm. On the one hand, whole journey need not artifical the participation, has improved the service efficiency in target place, and on the other hand utilizes to have had surveillance camera head as the sensor part, need not to install passenger flow meter at the gate, has reduced the construction degree of difficulty in the aspect of installation and power supply, is favorable to the scale to deploy.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Fig. 9 is a schematic structural diagram of a device for determining a vacant seat ratio according to an embodiment of the present disclosure, and as shown in fig. 9, the device for determining a vacant seat ratio may include: the device comprises an acquisition module 91, a detection module 92, a coordinate determination module 93 and a vacant seat rate determination module 94;
the acquiring module 91 is used for acquiring images in a target place acquired by the camera; wherein the camera is arranged in the target place;
a detection module 92, configured to detect the image, and obtain a seat and a human body in the image;
a coordinate determination module 93, configured to determine a first pixel coordinate of a contact point between the seat and the ground, and determine a second pixel coordinate of a contact point between the foot of the human body and the ground; determining a first coordinate of the contact point of the seat and the ground in a ground plane coordinate system according to the first pixel coordinate, and determining a second coordinate of the contact point of the foot of the human body and the ground in the ground plane coordinate system according to the second pixel coordinate;
an unoccupied seat rate determining module 94, configured to determine, according to the first coordinate, a current number of seats in the target location, and determine, according to the second coordinate, a current number of users in the target location; and determining the current vacant seat rate of the target place according to the seat number and the number of users.
The determination apparatus for the empty seat rate provided by the embodiment shown in fig. 9 can be used to implement the technical solution of the method embodiment shown in fig. 1 in this specification, and the implementation principle and technical effects of the determination apparatus can be further referred to the related description in the method embodiment.
Fig. 10 is a schematic structural diagram of a device for determining a vacant seat ratio according to another embodiment of the present disclosure, and compared with the device for determining a vacant seat ratio shown in fig. 9, in the device for determining a vacant seat ratio shown in fig. 10, the coordinate determining module 93 may include: a coordinate extraction submodule 931, a calculation submodule 932 and a reconstruction submodule 933;
the coordinate extraction submodule 931 is configured to extract coordinates of an orthogonal vanishing point in the image by detecting a straight line segment in the image;
a calculation submodule 932 for calculating the focal length and the extrinsic parameter matrix of the camera according to the coordinates of the orthogonal vanishing point; obtaining a change matrix of the image and the camera coordinate system according to the external parameter matrix and the height of the camera from the ground;
and the reconstruction submodule 933 is configured to perform three-dimensional semantic reconstruction according to the first pixel coordinate and the second pixel coordinate, the focal length of the camera, and the change matrix, so as to determine the first coordinate and the second coordinate.
Further, the device for determining the free seating rate may further include: an extraction module 95, a matching module 96 and a scene stitching module 97;
the extracting module 95 is configured to, when the number of the cameras is at least two, extract feature points from the images acquired by the at least two cameras after the acquiring module 91 acquires the images in the target location acquired by the cameras;
a matching module 96, configured to match the feature points extracted by the extraction module 95 to obtain feature point matching pairs;
and a scene stitching module 97, configured to determine a transformation relationship between the at least two cameras according to the feature point matching pairs, so as to perform scene stitching on the images acquired by the at least two cameras.
Further, the device for determining the free seating rate may further include: a retention module 98;
a reserving module 98, configured to perform non-maximum suppression processing on at least two first coordinates when at least two first coordinates are obtained through detection in an area where the seat is located, and reserve a first coordinate with a highest confidence level in the area where the seat is located as a coordinate of a ground plane coordinate system of a contact point between the seat and the ground; and/or when the human body is detected in the area to obtain at least two second coordinates, performing non-maximum suppression processing on the at least two second coordinates, and keeping the second coordinates with the highest confidence level in the area to be the coordinates of the contact point of the foot of the human body and the ground in the ground plane coordinate system.
In this embodiment, the first coordinate may include at least two coordinates of a first seat obtained by detecting the first seat in N consecutive images before the current time; further, the device for determining the free seating rate may further include: a retention module 98;
and a retaining module 98, configured to perform non-maximum suppression processing on at least two coordinates of the first seat after the coordinate determining module 93 determines the first coordinate of the contact point between the seat and the ground in the ground plane coordinate system according to the first pixel coordinate, and retain the coordinate with the highest confidence level of the at least two coordinates of the first seat as the coordinate of the contact point between the first seat and the ground in the ground plane coordinate system.
The determination apparatus for the unoccupied seat rate provided in the embodiment shown in fig. 10 can be used to implement the technical solutions of the method embodiments shown in fig. 1 to fig. 8 of the present application, and the implementation principles and technical effects thereof can be further described with reference to the related descriptions in the method embodiments.
Fig. 11 is a schematic structural diagram of an electronic device provided in an embodiment of the present specification, where as shown in fig. 11, the electronic device may include at least one processor; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the method for determining the unoccupied seat rate provided by the embodiments shown in fig. 1 to 8 in the present specification.
The electronic device can be a server, the server can be arranged at the cloud end, and the form of the electronic device is not limited in the embodiment.
FIG. 11 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present specification. The electronic device shown in fig. 11 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present specification.
As shown in fig. 11, the electronic device is embodied in the form of a general purpose computing device. Components of the electronic device may include, but are not limited to: one or more processors 410, a communication interface 420, a memory 430, and a communication bus 440 that connects the various components (including the memory 430, the communication interface 420, and the processing unit 410).
Electronic devices typically include a variety of computer system readable media. Such media may be any available media that is accessible by the electronic device and includes both volatile and nonvolatile media, removable and non-removable media.
A program/utility having a set (at least one) of program modules, including but not limited to an operating system, one or more application programs, other program modules, and program data, may be stored in memory 430, each of which examples or some combination may include an implementation of a network environment. The program modules generally perform the functions and/or methods of the embodiments described in fig. 1-8 herein.
The processor 410 executes various functional applications and data processing by executing programs stored in the memory 430, for example, implementing the seat occupancy determination method provided by the embodiments shown in fig. 1 to 8 in this specification.
The embodiments of the present specification provide a non-transitory computer-readable storage medium storing computer instructions, which cause the computer to execute the method for determining the unoccupied seat rate provided by the embodiments shown in fig. 1 to 8 of the present specification.
The non-transitory computer readable storage medium described above may take any combination of one or more computer readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer 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 computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, 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. In the context of this document, a computer 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.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer 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 computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, Radio Frequency (RF), etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present description may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
In the description of the specification, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present specification, "a plurality" means at least two, e.g., two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present description in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present description.
The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
It should be noted that the terminal referred to in the embodiments of the present specification may include, but is not limited to, a Personal Computer (PC), a Personal Digital Assistant (PDA), a wireless handheld device, a tablet computer (tablet computer), a mobile phone, an MP3 player, an MP4 player, and the like.
In the several embodiments provided in this specification, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions in actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present description may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods described in the embodiments of the present disclosure. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only a preferred embodiment of the present disclosure, and should not be taken as limiting the present disclosure, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
Claims (12)
1. A method for determining a vacant seat rate comprises the following steps:
acquiring an image in a target place acquired by a camera; wherein the camera is arranged in the target site;
detecting the image to obtain a seat and a human body in the image;
determining a first pixel coordinate of a contact point of the seat and the ground, and determining a second pixel coordinate of a contact point of the foot of the human body and the ground;
determining a first coordinate of the contact point of the seat and the ground in a ground plane coordinate system according to the first pixel coordinate, and determining a second coordinate of the contact point of the foot of the human body and the ground in the ground plane coordinate system according to the second pixel coordinate;
determining the current seat number in the target place according to the first coordinate, and determining the current user number in the target place according to the second coordinate;
and determining the current vacant seat rate of the target place according to the seat number and the number of users.
2. The method of claim 1, wherein said determining a first coordinate of the seat-to-ground contact point in a ground plane coordinate system from the first pixel coordinates and a second coordinate of the foot-to-ground contact point of the human body in the ground plane coordinate system from the second pixel coordinates comprises:
extracting coordinates of orthogonal vanishing points in the image by detecting straight line segments in the image;
calculating the focal length and the external parameter matrix of the camera according to the coordinate of the orthogonal vanishing point;
obtaining a change matrix of the image and the camera coordinate system according to the external reference matrix and the height of the camera from the ground;
and performing three-dimensional semantic reconstruction according to the first pixel coordinate and the second pixel coordinate, the focal length of the camera and the change matrix to determine the first coordinate and the second coordinate.
3. The method of claim 1 or 2, wherein the number of cameras is at least two; after the image in the target place that obtains the camera collection still includes:
extracting feature points from the images collected by the at least two cameras;
matching the extracted feature points to obtain feature point matching pairs;
and determining a transformation relation between the at least two cameras according to the feature point matching pair so as to splice the images acquired by the at least two cameras.
4. The method of claim 1, wherein said determining a first coordinate of said seat-to-ground contact point in a ground plane coordinate system based on said first pixel coordinate and a second coordinate of said human body foot-to-ground contact point in a ground plane coordinate system based on said second pixel coordinate further comprises:
when at least two first coordinates are obtained in the area where the seat is located through detection, carrying out non-maximum suppression processing on the at least two first coordinates, and reserving the first coordinate with the highest built-in reliability in the area where the seat is located as the coordinate of the contact point of the seat and the ground in the ground plane coordinate system; and/or the presence of a gas in the gas,
and when at least two second coordinates are obtained in the region where the human body is located through detection, performing non-maximum suppression processing on the at least two second coordinates, and reserving the second coordinate with the highest confidence level in the region where the human body is located as the coordinate of the contact point of the foot of the human body and the ground in the ground plane coordinate system.
5. The method of claim 1, wherein the first coordinates comprise at least two coordinates of a first seat obtained by detecting the first seat in consecutive N frames of images before the current time;
after determining the first coordinate of the seat-to-ground contact point in the ground plane coordinate system according to the first pixel coordinate, the method further includes:
and carrying out non-maximum suppression processing on at least two coordinates of the first seat, and reserving the coordinate with the highest confidence degree in the at least two coordinates of the first seat as the coordinate of the contact point of the first seat and the ground in a ground plane coordinate system.
6. An empty seat rate determination apparatus, comprising:
the acquisition module is used for acquiring images in a target place acquired by the camera; wherein the camera is arranged in the target site;
the detection module is used for detecting the image and acquiring a seat and a human body in the image;
the coordinate determination module is used for determining first pixel coordinates of a contact point of the seat and the ground and determining second pixel coordinates of a contact point of the foot of the human body and the ground; determining a first coordinate of the contact point of the seat and the ground in a ground plane coordinate system according to the first pixel coordinate, and determining a second coordinate of the contact point of the foot of the human body and the ground in the ground plane coordinate system according to the second pixel coordinate;
the unoccupied seat rate determining module is used for determining the current seat number in the target place according to the first coordinate and determining the current user number in the target place according to the second coordinate; and determining the current vacant seat rate of the target place according to the seat number and the number of users.
7. The apparatus of claim 6, wherein the coordinate determination module comprises:
the coordinate extraction submodule is used for extracting the coordinates of the orthogonal vanishing points in the image by detecting straight line segments in the image;
the calculation submodule is used for calculating the focal length and the external parameter matrix of the camera according to the coordinate of the orthogonal vanishing point; obtaining a change matrix of the image and the camera coordinate system according to the external parameter matrix and the height of the camera from the ground;
and the reconstruction submodule is used for performing three-dimensional semantic reconstruction according to the first pixel coordinate, the second pixel coordinate, the focal length of the camera and the change matrix so as to determine the first coordinate and the second coordinate.
8. The apparatus of claim 6 or 7, further comprising:
the extraction module is used for extracting feature points from the images collected by the at least two cameras after the acquisition module acquires the images in the target place collected by the cameras when the number of the cameras is at least two;
the matching module is used for matching the characteristic points extracted by the extraction module to obtain characteristic point matching pairs;
and the scene splicing module is used for determining the transformation relation between the at least two cameras according to the feature point matching pairs so as to splice the scenes of the images acquired by the at least two cameras.
9. The apparatus of claim 6, further comprising:
the reservation module is used for carrying out non-maximum suppression processing on at least two first coordinates when the at least two first coordinates are obtained by detection in the area where the seat is located, and reserving the first coordinate with the highest built-in reliability in the area where the seat is located as the coordinate of the contact point between the seat and the ground in the ground plane coordinate system; and/or when at least two second coordinates are obtained in the region where the human body is located through detection, performing non-maximum suppression processing on the at least two second coordinates, and reserving the second coordinate with the highest confidence level in the region where the human body is located as the coordinate of the contact point of the foot of the human body and the ground in the ground plane coordinate system.
10. The apparatus of claim 6, wherein the first coordinates comprise at least two coordinates of a first seat obtained by detecting the first seat in consecutive N frames of images before the current time;
the device further comprises:
and the retaining module is used for performing non-maximum suppression processing on at least two coordinates of the first seat after the coordinate determination module determines the first coordinate of the contact point between the seat and the ground in the ground plane coordinate system according to the first pixel coordinate, and retaining the coordinate with the highest confidence among the at least two coordinates of the first seat as the coordinate of the contact point between the first seat and the ground in the ground plane coordinate system.
11. An electronic device, comprising:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1 to 5.
12. A non-transitory computer readable storage medium storing computer instructions that cause the computer to perform the method of any of claims 1 to 5.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116388668A (en) * | 2023-03-30 | 2023-07-04 | 兰州理工大学 | Photovoltaic module cleaning robot with straddle travelling mechanism and cleaning method |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104217206A (en) * | 2013-05-31 | 2014-12-17 | 上海亚视信息科技有限公司 | Real-time attendance counting method based on high-definition videos |
| CN105550663A (en) * | 2016-01-07 | 2016-05-04 | 北京环境特性研究所 | Cinema attendance statistical method and system |
| US20160239714A1 (en) * | 2013-10-09 | 2016-08-18 | Nec Corporation | Passenger counting device, passenger counting method, and program recording medium |
| CN107122698A (en) * | 2016-07-19 | 2017-09-01 | 安徽大学 | A kind of real-time attendance statistical method of cinema based on convolutional neural networks |
| CN109300159A (en) * | 2018-09-07 | 2019-02-01 | 百度在线网络技术（北京）有限公司 | Method for detecting position, device, equipment, storage medium and vehicle |
| CN110941984A (en) * | 2019-09-25 | 2020-03-31 | 西南科技大学 | Study room seat state detection method and seat management system based on deep learning |
| CN111241993A (en) * | 2020-01-08 | 2020-06-05 | 咪咕文化科技有限公司 | Seat number determination method and device, electronic equipment and storage medium |
| JP6788710B1 (en) * | 2019-08-01 | 2020-11-25 | エコモット株式会社 | Image output device and image output method |
| CN112017246A (en) * | 2019-05-28 | 2020-12-01 | 北京地平线机器人技术研发有限公司 | Image acquisition method and device based on inverse perspective transformation |
| CN112288853A (en) * | 2020-10-29 | 2021-01-29 | 字节跳动有限公司 | Three-dimensional reconstruction method, three-dimensional reconstruction device, and storage medium |
| WO2021026705A1 (en) * | 2019-08-09 | 2021-02-18 | 华为技术有限公司 | Matching relationship determination method, re-projection error calculation method and related apparatus |
-
2021
- 2021-09-17 CN CN202111090916.7A patent/CN113792674B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104217206A (en) * | 2013-05-31 | 2014-12-17 | 上海亚视信息科技有限公司 | Real-time attendance counting method based on high-definition videos |
| US20160239714A1 (en) * | 2013-10-09 | 2016-08-18 | Nec Corporation | Passenger counting device, passenger counting method, and program recording medium |
| CN105550663A (en) * | 2016-01-07 | 2016-05-04 | 北京环境特性研究所 | Cinema attendance statistical method and system |
| CN107122698A (en) * | 2016-07-19 | 2017-09-01 | 安徽大学 | A kind of real-time attendance statistical method of cinema based on convolutional neural networks |
| CN109300159A (en) * | 2018-09-07 | 2019-02-01 | 百度在线网络技术（北京）有限公司 | Method for detecting position, device, equipment, storage medium and vehicle |
| CN112017246A (en) * | 2019-05-28 | 2020-12-01 | 北京地平线机器人技术研发有限公司 | Image acquisition method and device based on inverse perspective transformation |
| JP6788710B1 (en) * | 2019-08-01 | 2020-11-25 | エコモット株式会社 | Image output device and image output method |
| WO2021026705A1 (en) * | 2019-08-09 | 2021-02-18 | 华为技术有限公司 | Matching relationship determination method, re-projection error calculation method and related apparatus |
| CN110941984A (en) * | 2019-09-25 | 2020-03-31 | 西南科技大学 | Study room seat state detection method and seat management system based on deep learning |
| CN111241993A (en) * | 2020-01-08 | 2020-06-05 | 咪咕文化科技有限公司 | Seat number determination method and device, electronic equipment and storage medium |
| CN112288853A (en) * | 2020-10-29 | 2021-01-29 | 字节跳动有限公司 | Three-dimensional reconstruction method, three-dimensional reconstruction device, and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 储文静;储昭辉;许晓云;: "基于座位管理系统数据的图书馆读者自习行为探究", 图书馆研究, no. 05, 30 September 2017 (2017-09-30) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116388668A (en) * | 2023-03-30 | 2023-07-04 | 兰州理工大学 | Photovoltaic module cleaning robot with straddle travelling mechanism and cleaning method |
| CN116388668B (en) * | 2023-03-30 | 2024-03-12 | 兰州理工大学 | Photovoltaic module cleaning robot with straddle travelling mechanism and cleaning method |
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|---|---|
| CN113792674B (en) | 2024-03-26 |
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