CN112465860A - Method and equipment for checking running state of door - Google Patents
Method and equipment for checking running state of door Download PDFInfo
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- CN112465860A CN112465860A CN202011287304.2A CN202011287304A CN112465860A CN 112465860 A CN112465860 A CN 112465860A CN 202011287304 A CN202011287304 A CN 202011287304A CN 112465860 A CN112465860 A CN 112465860A
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- 238000007689 inspection Methods 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000013135 deep learning Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
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- 238000010586 diagram Methods 0.000 description 3
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
Abstract
The invention relates to a method for checking the operating state of a door, comprising: s1, acquiring image data of a door, and extracting a target area on the door based on the image data; s2, acquiring the image data according to a time sequence, and acquiring a motion track of the target area based on the image data; s3, verifying the running track; and S4, acquiring the image data of the door again, and tracking the running state of the door based on the verified motion track. The detection method of the scheme has the advantages of high efficiency, excellent accuracy and good real-time performance.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and equipment for checking the running state of a door.
Background
The elevator is a very popular public facility, is widely applied to scenes such as markets, office areas, residential areas and the like, brings great convenience to daily life of people, and occasionally breaks down due to the influence of factors such as incorrect use, untimely maintenance and the like. The door fault is the fault with the highest occurrence frequency in a plurality of faults of the elevator, the types of the door faults are more, the door cannot be opened normally after the elevator is stopped, people are trapped or passengers are clamped, the door opening in the elevator running process can cause the occurrence of a passenger falling event, therefore, the door motion information can be acquired in real time, the door motion information is analyzed, the elevator with abnormal door motion is maintained in time, and the elevator taking safety problem caused by the door fault can be effectively reduced.
The prior art is as follows: and determining the door edge position by using an identification method, predicting the door edge position by using a door motion model, and obtaining a door motion track by combining the identification and prediction methods. The problems existing in the prior art are as follows: 1. the elevator door edge identification speed is slow, and real-time identification is difficult to achieve; 2. false detection easily occurs when illumination or background color and a door are close, and the result is unstable; 3. when the door is about to be fully opened, typically due to specular reflection, there are often multiple edges present, the edge locations of which are difficult to determine.
For example, chinese patent CN107886524A discloses a method for identifying a motion trajectory of an elevator door. The method determines the position of the door edge in the elevator door image sequence in an identification mode; determining an elevator door motion track model according to the acquired elevator door edge position information; and then determining the motion track of the elevator door by combining a door edge identification method and a track model. However, the technical solution has the following disadvantages:
the method comprises the following steps of extracting the fact that the door edge is easily interfered by the outside, so that the accuracy and the stability of the motion track of the door are influenced, and the influence factors mainly include the influence of ambient light, the background information outside the elevator and the shielding of passengers on the elevator door edge, so that the complete door edge cannot be detected; the door edge extraction efficiency is also low, making real-time applications difficult to meet at the camera end.
Disclosure of Invention
The invention aims to provide a method and equipment for checking the running state of a door, which solve the problem of low detection efficiency.
To achieve the above object, the present invention provides an operation state checking method for a door, comprising:
s1, acquiring image data of a door, and extracting a target area on the door based on the image data;
s2, acquiring the image data according to a time sequence, and acquiring a motion track of the target area based on the image data;
s3, verifying the running track;
and S4, acquiring the image data of the door again, and tracking the running state of the door based on the verified motion track.
According to an aspect of the present invention, in step S2, the motion profile is obtained based on the door-closed state of the door as the start.
According to an aspect of the present invention, in step S2, in the step of acquiring the motion trajectory of the target area from the image data, the target areas in the adjacent image data are searched in time series, and the positions of the target areas are acquired and sequentially arranged in time series to form the motion trajectory.
According to an aspect of the present invention, the step of acquiring the motion trajectory of the target region based on the image data in step S2 includes:
s21, establishing a coordinate axis parallel to the opening and closing direction of the door based on the image data of the door;
s22, acquiring corresponding nodes of all positions on the running track on the coordinate axis along the direction perpendicular to the coordinate axis;
and S23, sequentially acquiring the deviation distance between each position on the running track and the corresponding node according to a time sequence from a door closing state to a door opening state.
According to an aspect of the invention, in the step of verifying the moving trajectory in step S3, a starting point of the moving trajectory is a position of the target area on the image data when the door is closed.
According to an aspect of the present invention, the step of retrieving the image data of the door and tracking the operation state of the door based on the verified motion trajectory in step S4 includes:
s41, acquiring the image data of the door again, judging whether the door in the image data is in a door closing state, and if so, starting to perform tracking;
s42, acquiring the image data according to a time sequence from a door closing state to a door opening state and correcting the image data based on the deviation distance;
s43, acquiring the corrected target area in the image data based on a tracking algorithm, and performing matching judgment on the target area and corresponding nodes on a coordinate axis to realize tracking of the running state of the door.
According to an aspect of the present invention, in step S2, the motion trajectory is obtained using a KCF tracking algorithm or a deep learning based tracking algorithm.
According to an aspect of the invention, the target region is extracted from the image data by means of manual and/or automatic analysis.
To achieve the above object, the present invention provides an inspection apparatus comprising:
the image acquisition device is used for acquiring an image data acquisition module and a tracking module of the image data of the door;
the server is used for acquiring the image data, processing the image data to acquire the running track and sending the running track to the tracking module;
the tracking module tracks the running state of the door based on the image data collected by the image data collecting module and generates motion information.
According to one aspect of the invention, after receiving the operation track, the tracking module determines whether the door is in a closed state, and if so, performs algorithm initialization and starts to perform tracking.
According to an aspect of the present invention, the deviation distance of the corresponding node on the coordinate axis parallel to the opening and closing direction of the door is obtained by the server or obtained by the tracking module.
According to one scheme of the invention, the server side and the image acquisition side resources are fused in the scheme, firstly, a tracking method with high complexity and good effect is used at the server side to obtain an accurate door running track, then the generated track is sent to the image acquisition side, and the image acquisition side combines a simple tracking algorithm with the track to accurately obtain door motion information in real time.
According to the scheme of the invention, the calculated amount of the camera end can be effectively reduced, the tracking accuracy is improved, and the edge position of the door can be well determined through the accurate track of the server end.
According to one scheme of the invention, a tracking method (discriminant) based on feature matching is adopted, and the method is small in calculation amount and suitable for being applied to a camera end.
Drawings
FIG. 1 is a block diagram that schematically illustrates the steps of a status checking method, in accordance with an embodiment of the present invention;
FIG. 2 is a schematic representation of image data acquired in a condition inspection method according to one embodiment of the present invention;
FIG. 3 is a schematic representation of a mark position containing a target area represented in a status checking method according to one embodiment of the present invention;
FIG. 4 is a view schematically showing a target area in a door-closed state in a state checking method according to an embodiment of the present invention;
FIG. 5 is a schematic representation of a travel path of a door in a status checking method according to an embodiment of the present invention;
fig. 6 is a view schematically showing a running track of a door displayed in image data in a status checking method according to an embodiment of the present invention;
fig. 7 is a correction diagram schematically showing a running locus of a door displayed in image data in a state checking method according to an embodiment of the present invention;
FIG. 8 is a schematic representation of a correction trajectory in a condition checking method according to an embodiment of the present invention;
fig. 9 is a block diagram schematically showing an inspection apparatus according to an embodiment of the present invention.
Detailed Description
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 embodiments 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 them without inventive effort.
In describing embodiments of the present invention, the terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in an orientation or positional relationship that is based on the orientation or positional relationship shown in the associated drawings, which is for convenience and simplicity of description only, and does not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus, the above-described terms should not be construed as limiting the present invention.
The present invention is described in detail below with reference to the drawings and the specific embodiments, which are not repeated herein, but the embodiments of the present invention are not limited to the following embodiments.
As shown in fig. 1, according to an embodiment of the present invention, an operation state checking method for a door of the present invention includes:
s1, acquiring image data of a door, and extracting a target area on the door based on the image data;
s2, acquiring image data according to a time sequence, and acquiring a motion track of a target area based on the image data;
s3, verifying the running track;
and S4, acquiring the image data of the door again, and tracking the running state of the door based on the verified motion track.
As shown in fig. 2, in step S1, image data of an area where a door (e.g., an elevator door) is located is obtained by an image capturing device, and the captured data is stored in a server.
As shown in fig. 3, in this embodiment, the position of the door including the target area (e.g., the area where the tag is located) is manually set according to the acquired image data, and/or the position of the door including the target area (e.g., the area where the tag is located) is set in an automatic analysis manner, so as to obtain the position including the target area (i.e., a polygon position with rich texture shown in fig. 3). Further, a target area having a target on each door is identified based on the location containing the target area (see fig. 4).
According to an embodiment of the present invention, in step S2, the image data in the server is sequentially extracted according to the time sequence of the image data and the target area in the image data is acquired (see the box position in fig. 4). The movement locus of the target area during the opening and closing of the door can be formed by changing the position of the target area according to the time-series change (see fig. 5). In the present embodiment, a tracking algorithm is used to track a target region and form a motion trajectory. Furthermore, as shown in fig. 5 and fig. 6, a target area in the image data in the server is a tracking target of a tracking algorithm, data images are extracted according to a time sequence, the target area between two adjacent frames of images (certainly, two non-adjacent frames of images) is searched through the tracking algorithm, the similarity between the target areas is compared, and a target area with the highest similarity is obtained, so that continuous tracking of the target area in the image data can be continuously achieved, and the purpose of generating a running track is further achieved. In the present embodiment, in the process of generating the movement trajectory by performing the tracking algorithm, if the door in the input image data is in the closed state (see fig. 4), the movement trajectory of the target area can be directly formed by the operation process of the door from closed to open. If the door in the input image data is in a non-closed door state, it is difficult to directly acquire a complete running track through the running state of the door, and further the image data needs to be continuously extracted according to time sequence to execute the tracking process of the tracking algorithm until the running track can be extracted when the door is in a closed state. In this embodiment, the process of generating the movement track is implemented on the server, which belongs to an offline generation process, and the adopted tracking algorithm may be a KCF tracking algorithm or a tracking algorithm based on deep learning, so as to finally obtain the movement track of the door, and the precision of the track is very high. In this embodiment, the target area is lost when the door is fully opened, and the last target area position that can be tracked is the door boundary.
According to an embodiment of the present invention, the step of acquiring the motion trajectory of the target region based on the image data in step S2 includes:
s21, establishing a coordinate axis parallel to the opening and closing direction of the door based on the image data of the door; in the present embodiment, referring to fig. 5 to 7, a coordinate axis a parallel to the opening and closing direction of the door is established based on the image data of the door, and the coordinate axis a may be displayed or hidden in the image data, which may be represented by a straight line having a color.
S22, acquiring corresponding nodes of all positions on a coordinate axis on a running track along a direction vertical to the coordinate axis; in the present embodiment, as shown in fig. 5 to 7, the corresponding position in the vertical direction with respect to the coordinate axis, that is, the corresponding node is calculated based on the target region in the travel locus.
And S23, sequentially acquiring the deviation distance between each position on the running track and the corresponding node according to a time sequence from a door closing state to a door opening state. In the present embodiment, a deviation distance value between the target area on the trajectory and the corresponding node b on the coordinate axis is acquired.
In this embodiment, in step S3, in the step of verifying the trajectory, the start point and the end point of the trajectory are both in the known target region, and the situation that there is a sudden large change in the slope of the trajectory is eliminated. Furthermore, the starting point of the movement track is the position of the target area on the image data when the door is closed, that is, when the door is closed again, the target area can return to the starting position through verification.
And after the verification, obtaining an accurate running track for opening and closing the door, and sending the accurate running track to a camera end.
According to an embodiment of the present invention, in step S4, the step of retrieving the image data of the door, and tracking the operation state of the door based on the verified motion trajectory includes directly tracking the operation state of the door on a line through a camera terminal, including:
s41, acquiring the image data of the door again, judging whether the door in the image data is in a door closing state, and if so, starting to perform tracking. In this embodiment, the camera end acquires the real-time image data of the door again on line, and determines whether the door is closed, and if so, initializes the tracking algorithm on the camera end to start tracking.
And S42, acquiring the image data according to a time sequence from a door closing state to a door opening state, and correcting the image data based on the deviation distance. Referring to fig. 2 and 3, due to the influence of the installation position of the image acquisition device and the imaging quality, when the door is imaged in the operation process, the position of the target area on the door still changes in the horizontal direction and the vertical direction in the opening and closing process, and then the camera end is required to correct the image data in the process of acquiring the image so as to realize the horizontal real-time tracking based on the door opening and closing direction. In the present embodiment, the deviation distances δ h (see fig. 5) between each position on the movement trajectory and the horizontal coordinate axis in the vertical direction are calculated by the server in time series during the process of acquiring the movement trajectory, and these deviation distances δ h are also sent to the camera end together to be used as parameters for correcting the target area. It is noted that δ h is equal to the height of a point of a track per column and the pixel height difference of the track starting point (i.e. the vertical distance between the point of the track per column and the horizontal coordinate axis). In addition, according to another embodiment of the present invention, after the server issues the motion trajectory to the camera end, the offset distance may be directly calculated and obtained by the camera end.
In the present embodiment, the entire image is adjusted based on the offset distance value corresponding to each node b on the movement trajectory on the camera side to realize the correction of the image data, as shown in fig. 8, by the above-described correction method, the method in which the target areas are sequentially arranged in time series and coordinate axes on each image data is actually realized after the image correction is performed in time series, and further, the displacement in the vertical direction is effectively eliminated, and only the position change in the coordinate axis direction (horizontal direction) is provided.
Specifically, when the camera starts to perform tracking, a first frame of image (such as a gate-off state) is acquired according to the time sequence camera, and each column of nodes (such as pixel points) in the image data is corrected in the first frame of image data based on the deviation distance between the running track and each node (such as pixel point) on the horizontal coordinate axis, so that the subsequent matching judgment process can be performed.
Through the correction process, the tracking of the running track in the one-dimensional direction (horizontal direction) is converted from the tracking in the two-dimensional direction (horizontal direction and vertical direction), the calculation amount of a tracking algorithm is greatly reduced, and the efficiency and the effect of on-line tracking are greatly improved.
S43, acquiring a target area in the corrected image data based on a tracking algorithm, and performing matching judgment on the target area and corresponding nodes on a coordinate axis to realize tracking of the running state of the door. In the embodiment, after the image data is corrected, the target area in the image data is obtained, and the actual operation position of the door can be obtained only by matching and judging the obtained target area and the position of the corresponding node on the coordinate axis, so that the actual operation position of the door can be completely obtained by correcting continuous time sequence image data (namely, video stream) and matching and judging the position, and the tracking of the operation state of the door is achieved. In this embodiment, the tracking algorithm used by the camera end is a simpler tracking algorithm, such as a tracking algorithm based on hash matching.
In this embodiment, in the process of tracking at the camera end, the manner of acquiring the target region in the image is the same as the foregoing manner, and details are not repeated here.
It should be noted that, because the movement track is generated after a plurality of image data are continuously collected and extracted, the movement track may be displayed in different image data in an overlapping manner, or may not be displayed in an overlapping manner.
It should be noted that, in the foregoing tracking algorithm, when the tracking algorithm is initialized (i.e. in the door-closed state), an area of interest is set in a certain frame of image, the area is the target to be tracked, and after the subsequent target moves, the position after the target moves can be found again near the target in the previous frame by the above method.
As shown in fig. 9, according to an embodiment of the present invention, an inspection apparatus of the present invention includes: the image acquisition device and the server connected with the image acquisition device. The image acquisition device and the server can be connected in a wired or wireless mode. In the embodiment, the image acquisition device can adopt a camera, an image data acquisition module and a tracking module for acquiring the image data of the door; the server is used for acquiring the image data, processing the image data to acquire the running track and sending the running track to the tracking module. In the present embodiment, the tracking module tracks the operation state of the door based on the image data acquired by the image data acquisition module and generates motion information.
As shown in fig. 1, according to an embodiment of the present invention, after receiving the operation trajectory, the tracking module determines whether the door is closed, and if so, performs algorithm initialization and starts to perform tracking.
In the embodiment, in the process of tracking the door, the motion information can be analyzed, and if abnormal, the alarm information is output in real time.
In this embodiment, the deviation distance of the corresponding node on the coordinate axis parallel to the switching direction of the gate is obtained by the server or the tracking module.
The foregoing is merely exemplary of particular aspects of the present invention and devices and structures not specifically described herein are understood to be those of ordinary skill in the art and are intended to be implemented in such conventional ways.
The above description is only one embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (11)
1. An operation state checking method for a door, comprising:
s1, acquiring image data of a door, and extracting a target area on the door based on the image data;
s2, acquiring the image data according to a time sequence, and acquiring a motion track of the target area based on the image data;
s3, verifying the running track;
and S4, acquiring the image data of the door again, and tracking the running state of the door based on the verified motion track.
2. The operation state checking method according to claim 1, wherein in step S2, the motion trajectory is acquired based on a door-closed state of the door as a start.
3. The operation status checking method according to claim 2, wherein in the step of acquiring the movement locus of the target region by the image data in step S2, the target regions in the adjacent image data are searched in time series, and the positions of the target regions are acquired and sequentially arranged in time series to constitute the movement locus.
4. The operation status checking method according to claim 3, wherein the step of obtaining the motion trajectory of the target region based on the image data in step S2 includes:
s21, establishing a coordinate axis parallel to the opening and closing direction of the door based on the image data of the door;
s22, acquiring corresponding nodes of all positions on the running track on the coordinate axis along the direction perpendicular to the coordinate axis;
and S23, sequentially acquiring the deviation distance between each position on the running track and the corresponding node according to a time sequence from a door closing state to a door opening state.
5. The operation state checking method according to claim 3, wherein in the step of verifying the running locus in step S3, a starting point of the running locus is a position of the target area on the image data when the door is in a closed door state.
6. The operation state checking method according to claim 5, wherein the step of retrieving the image data of the door in step S4 and tracking the operation state of the door based on the verified motion trajectory includes:
s41, acquiring the image data of the door again, judging whether the door in the image data is in a door closing state, and if so, starting to perform tracking;
s42, acquiring the image data according to a time sequence from a door closing state to a door opening state and correcting the image data based on the deviation distance;
s43, acquiring the corrected target area in the image data based on a tracking algorithm, and performing matching judgment on the target area and corresponding nodes on a coordinate axis to realize tracking of the running state of the door.
7. The operation state checking method according to any one of claims 1 to 4, wherein in step S2, the motion trajectory is acquired using a KCF tracking algorithm or a deep learning-based tracking algorithm.
8. An operation status checking method according to any one of claims 1 to 4, wherein the target region is extracted from the image data by means of manual and/or automatic analysis.
9. An inspection apparatus used in the operation state inspection method according to any one of claims 1 to 8, characterized by comprising:
the image acquisition device is used for acquiring an image data acquisition module and a tracking module of the image data of the door;
the server is used for acquiring the image data, processing the image data to acquire the running track and sending the running track to the tracking module;
the tracking module tracks the running state of the door based on the image data collected by the image data collecting module and generates motion information.
10. The inspection apparatus of claim 9, wherein the tracking module receives the trajectory and determines whether the door is closed, and if so, performs algorithm initialization and starts tracking.
11. The inspection apparatus according to claim 10, wherein the deviation distance of the corresponding node on the coordinate axis of the travel locus parallel to the opening and closing direction of the door is acquired by the server or by the tracking module.
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