CN114494983A - Railway foreign matter invasion monitoring method and system - Google Patents
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Abstract
The invention provides a method and a system for monitoring railway foreign matter invasion, and relates to the field of image recognition. Firstly, reading images of a video sequence, and defining a detection area in the images; judging whether the image is matched with a Gaussian mixture background model, if not, extracting a foreground object of the image; calculating the volume of a foreground object in the detection area; judging whether the volume is larger than a first threshold value or not, if so, judging that the foreign matter is invaded; and marking the position and the volume of the invasive foreign body. Determining a foreground object of an image by judging whether the acquired image is matched with a Gaussian mixture background model; and further judging whether the foreground image is an invasive foreign object or not based on the volume of the foreground image. The invasion foreign matter in the tunnel can be effectively identified, the alarm is given to the working personnel in time, and the accident is avoided.
Description
Technical Field
The present disclosure relates to the field of image recognition, and in particular, to a method and a system for monitoring intrusion of foreign objects into a railway, a medium and a device.
Background
In recent years, the country has vigorously developed the infrastructure construction of high-speed railways, and the four-longitudinal four-transverse' express railway network has been developed in an initial scale, so that great convenience is brought to people for going out. Due to the characteristic that the high-speed railway is high in running speed, the curve radius of the high-speed railway is determined to be large, the slope rate is determined to be low, and therefore the tunnel proportion in the high-speed railway is heavy. Due to the closeness of tunnels, railway related safety accidents also sometimes occur, particularly derailment and rollover of trains caused by natural disasters, which indicate the necessity of inspection and early warning along the railway.
The disturbance of tunnel country rock may be very big that natural disasters or peripheral blasting arouse, cause the country rock more loose, can lead to the tunnel country rock step to drop, and the safe operation of train in the tunnel can not obtain the guarantee like this. Therefore, it is an urgent problem to find a monitoring method for detecting the block falling in the railway tunnel more quickly and intelligently.
Disclosure of Invention
In order to overcome the problems in the related art, the railway foreign matter intrusion monitoring method, the railway foreign matter intrusion monitoring system, the railway foreign matter intrusion monitoring medium and the railway foreign matter intrusion monitoring equipment are provided, and a foreground object of an image is determined by judging whether the acquired image is matched with a Gaussian mixed background model or not; and further judging whether the foreground image is an invasive foreign object or not based on the volume of the foreground image. The invasion foreign matter in the tunnel can be effectively identified, the alarm is given to the working personnel in time, and the accident is avoided.
According to a first aspect herein, there is provided a railway foreign object intrusion monitoring method comprising: reading an image of a video sequence, delimiting a detection area in the image; judging whether the image is matched with a Gaussian mixed background model or not, and if not, extracting a foreground object of the image; calculating the volume of a foreground object in the detection area; judging whether the volume is larger than a first threshold value or not, and if so, judging that the foreign body is invaded; and marking the position and the volume of the invasive foreign body.
Based on the scheme, before judging whether the original image is matched with a Gaussian mixture background model or not, modeling the Gaussian mixture background model; each pixel of the background image is respectively modeled by a Gaussian mixture model formed by K Gaussian distributions, and the modeling of the Gaussian mixture background model specifically comprises the following steps:
wherein,
representing the value of the pixel j at the time t, if the pixel j is an RGB three-channel
In the form of a vector, the vector,
，
an estimate of the weight coefficients representing the ith gaussian distribution in the gaussian background model at time t,
the mean vector of the ith Gaussian distribution in the mixed Gaussian background model at the moment t is represented;
and (2) representing the covariance matrix of the ith Gaussian distribution in the mixed Gaussian background model at the moment t, and eta representing the probability density function of the Gaussian distribution.
Based on the scheme, the matching of the original image and the Gaussian mixture background model is judged, wherein the matching comprises pixel values
Defining the distance between the mean value of the ith Gaussian distribution in the Gaussian mixture background model and the standard deviation of the mean value of the ith Gaussian distribution to be less than 2.5 times of the standard deviation of the mean value of the ith Gaussian distribution in the Gaussian mixture background model
Matching; namely, it is
。
Based on the scheme, after the volume is judged to be larger than a first threshold value, the staying time of the foreground object is calculated, and if the staying time is larger than a second threshold value, the intrusion foreign matter is judged.
According to another aspect herein, there is provided a railway foreign object intrusion monitoring system comprising: the device comprises a detection unit, a detection unit and a processing unit, wherein the detection unit is used for reading images of a video sequence and demarcating a detection area in the images; the first judgment unit is used for judging whether the image is matched with the Gaussian mixture background model or not, and if not, extracting a foreground object of the image; a calculation unit for calculating the volume of a foreground object within the detection area; the second judgment unit is used for judging whether the volume is larger than the first threshold value or not, and if so, judging that the foreign body is invaded; and the marking unit is used for marking the position and the volume of the invasion foreign matter.
According to another aspect herein, there is provided a computer readable storage medium having stored thereon a computer program which, when executed, implements the above-mentioned railway foreign object intrusion monitoring method.
According to another aspect of the present document, there is provided a computer device comprising a processor, a memory and a computer program stored on the memory, the processor implementing the above mentioned steps of the railway foreign object intrusion method when executing the computer program.
The text provides a method and a system for monitoring railway foreign matter invasion. Firstly, reading images of a video sequence, and delimiting a detection area in the images; judging whether the image is matched with a Gaussian mixed background model or not, and if not, extracting a foreground object of the image; calculating the volume of a foreground object in the detection area; judging whether the volume is larger than a first threshold value or not, and if so, judging that the foreign body is invaded; and marking the position and the volume of the invasive foreign body. Determining a foreground object of an image by judging whether the acquired image is matched with a Gaussian mixture background model; and further judging whether the foreground image is an invasive foreign object or not based on the volume of the foreground image. The invasion foreign matter in the tunnel can be effectively identified, the alarm is given to the working personnel in time, and the accident is avoided.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification. In the drawings:
fig. 1 is a flow chart illustrating a method for monitoring intrusion of foreign objects into a railway according to an exemplary embodiment.
Fig. 2 is a flow chart illustrating a method for intrusion monitoring of foreign objects into a railway according to another exemplary embodiment.
Fig. 3 is a block diagram illustrating a foreign object intrusion apparatus for a railway according to an exemplary embodiment.
FIG. 4 is a block diagram illustrating a computer device according to an example embodiment.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some but not all of the embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments herein without making any creative effort, shall fall within the scope of protection. It should be noted that the embodiments and features of the embodiments may be arbitrarily combined with each other without conflict.
Unless the context clearly requires otherwise, throughout the description and the claims, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, what is meant is "including, but not limited to".
In the description of the present disclosure, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present disclosure, "a plurality" means two or more unless otherwise specified.
The foreign matter in the railway tunnel can be a large falling stone, the monitoring of the foreign matter invading in the tunnel is substantially to track a moving target, and the falling stone becomes a static object after rolling. Therefore, the research of the intrusion detection of the foreign matters in the tunnel is based on the moving target detection in video monitoring, and the intrusion detection of the foreign matters can be converted into the detection of moving objects. The invention provides a method and a system for monitoring railway foreign matter intrusion, wherein a foreground object of an image is determined by judging whether the acquired image is matched with a Gaussian mixture background model; and further judging whether the foreground image is an invasive foreign object or not based on the volume of the foreground image. The invasion foreign matter in the tunnel can be effectively identified, the alarm is given to the working personnel in time, and the accident is avoided.
Fig. 1 is a flow chart illustrating a method for railway foreign object intrusion monitoring according to an exemplary embodiment. Referring to fig. 1, the method for monitoring railway foreign object intrusion at least comprises the following steps:
step 101: reading an image of a video sequence, delimiting a detection area in the image;
specifically, an image of a video sequence refers to a continuous image frame within a certain time period; for example, successive frame images acquired by the camera for one hour. The invasion foreign matter in the present text generally refers to an object with a large volume, such as a falling large stone block, which may cause a safety hazard to the passing of a train, and the falling stone block has a certain volume range, and the stone block volume affecting the safety of the train has a certain lower limit. It is common that tunnel railways are closed and difficult for other moving animals to enter, apart from regular inspections by maintenance personnel. And the motion of other moving objects, such as livestock, is continuous and eventually not as static as a stone.
Further, a detection area is defined based on the layout of the cameras in the tunnel and the detection distance range. The layout system of the cameras in the tunnel comprises a static camera measurement and control device, a video data transmission line, video and optical fiber signal conversion equipment, an optical fiber cable and optical fiber and video signal conversion equipment.
In one embodiment, the detection distance range of each camera measurement and control device is 15m, each camera is provided with one monitoring section every 15m, each monitoring section is provided with 1 set of camera measurement and control system, each set of monitoring system comprises 7 camera measurement and control devices, and the coverage range is 105 m; the installation height of the camera measurement and control device is 1.5M relative to the vertical height of the surface of the cable groove, and the camera measurement and control device is rigidly fixed on the surface of the side wall of the tunnel through an M10 internal expansion bolt; the video data transmission line is protected by a PVC pipe with the diameter of 10mm and fixed on the side wall of the tunnel below the camera measurement and control device, and a data cable connected with the measurement and control device and the optical fiber conversion equipment is arranged in a cable groove.
The defined detection area may be a middle area based on an image captured by the camera, or an area in which the user is interested; the extraction of regions of interest from images is not limited to using OpenCV and Python. The demarcated detection area is the area most capable of showing the appearance of the foreign matter. Therefore, the whole image shot by the camera is prevented from being recognized, only the key image area is analyzed in a centralized manner, the workload of subsequent image processing is reduced, and the image recognition efficiency is improved.
Step 102: judging whether the image is matched with a Gaussian mixed background model or not, and if not, extracting a foreground object of the image;
in particular, a fixed camera views the scene, the background remaining almost unchanged. In this case, the element of interest is an object moving in the scene. We refer to these moving objects as foreground objects and in order to extract these foreground objects we need to model the image background and then compare the model of the current image frame with the background model to detect the foreground objects.
The Gaussian mixture background model modeling uses K (K is 3-5) Gaussian models to represent the characteristics of each pixel point in the image. The specific process of modeling the Gaussian mixture background model is as follows:
And matching each pixel point in the current image with the Gaussian mixture background model, if the matching is successful, judging the pixel point as a background point, and if the matching is not successful, judging the pixel point as a foreground point. The set of foreground points constitutes a foreground object. The matching condition is a pixel value
Defining the distance between the mean value of the ith Gaussian distribution in the Gaussian mixture background model and the standard deviation of the mean value of the ith Gaussian distribution to be less than 2.5 times of the standard deviation of the mean value of the ith Gaussian distribution in the Gaussian mixture background model
And (6) matching. Namely, it is
It should be noted that, the foreground image is extracted through the gaussian mixture background model, and the foreground object is focused and analyzed, so that the moving object can be accurately identified.
Step 103: calculating the volume of a foreground object in the detection area;
specifically, the shape characteristics of the foreground object do not vary much; the size of the moving object can be known by calculating the volume of the foreground object. Methods for calculating the image size that are conventional in the art include a calculation method based on the contour of the object shape, an OpenCV extraction method, and the like. The present disclosure is not limited in this particular manner.
Step 104: judging whether the volume is larger than a first threshold value or not, and if so, judging that the foreign body is invaded;
specifically, the invading foreign body generally refers to a large-volume object, such as a falling large stone block, which may cause a safety hazard to train traffic, and the falling stone block has a certain volume range, and the volume of the stone block affecting train safety has a certain lower limit. The first threshold may be set as a lower stone volume limit that affects train safety. Can be set by a person skilled in the art on the basis of practical experience.
Step 105: and marking the position and the volume of the invasive foreign body.
Specifically, when there is an invading foreign body, the invading foreign body is marked. The marking means may be conventional means commonly used in the art for image marking. For example, rectangular box labeling, key point labeling, point cloud labeling, and the like. And is not particularly limited herein. The marking position may be marked by the position of the camera.
Further, the position and the volume of the invading foreign matter are output to the monitoring terminal through the transmission line, the monitoring terminal gives an alarm to remind a worker to check in time, and the worker can be on site at the first time, so that accidents are avoided.
In the embodiment, the foreground object of the image is determined by judging whether the acquired image is matched with the Gaussian mixture background model; and further judging whether the foreground image is an invasive foreign object or not based on the volume of the foreground image. The invasion foreign matter in the tunnel can be effectively identified, the alarm is given to the working personnel in time, and the accident is avoided.
The second embodiment of the present application relates to a method for monitoring intrusion of foreign objects into a railway, which is a further supplement to the first embodiment, and adds other relevant steps to step 104, and specifically describes step 104.
As shown in fig. 2, the present embodiment includes steps 201 to 205. Step 201, step 202, step 203 and step 205 are substantially the same as step 101, step 102, step 103 and step 105 in the first embodiment, and are not described in detail here, and the following differences are mainly described:
step 204: and after the volume is judged to be larger than a first threshold value, calculating the retention time of the foreground object, and if the retention time is larger than a second threshold value, judging that the foreign object is invaded.
Specifically, in order to improve the accuracy of identifying the intrusion foreign matter, the imaging size and the retention time of the object appearing in the monitored area are used as important criteria for judging whether the intrusion foreign matter exists. An intruding object, such as a falling rock, can stay for a long time until it is cleaned. When the dwell time exceeds a second threshold value, for example 1 hour, 2 hours, the accuracy of identifying the foreign object is further improved on the basis of being identified as an intruding foreign object. In addition, when the stay time does not exceed the second threshold, the foreign object cannot be identified as an invasive foreign object, for example, an operator enters the monitoring range, and the foreign object cannot be identified as an invasive foreign object due to the short stay time. The second threshold value can be set according to actual needs. The embodiment can further improve the accuracy of identifying the invading foreign body.
Fig. 3 illustrates a railway foreign object intrusion monitoring system 30 according to an exemplary embodiment. The device 30 comprises:
a detection unit 301 for reading images of a video sequence, in which images detection areas are delimited;
a first determining unit 302, configured to determine whether the image is matched with a gaussian mixture background model, and if not, extract a foreground object of the image;
a calculating unit 303, configured to calculate a volume of a foreground object in the detection area;
a second determining unit 304, configured to determine whether the volume is greater than a first threshold, and if so, determine that a foreign object is invaded;
and a marking unit 305 for marking the position and volume of the invasive foreign object.
It should be understood that this embodiment is an example of the apparatus corresponding to the first embodiment, and may be implemented in cooperation with the first embodiment. The related technical details mentioned in the first embodiment are still valid in this embodiment, and are not described herein again in order to reduce repetition. Accordingly, the related-art details mentioned in the present embodiment can also be applied to the first embodiment.
Further, the first judging unit models the Gaussian mixture background model before judging whether the image is matched with the Gaussian mixture background model; and modeling the Gaussian mixture background model before judging whether the image is matched with the Gaussian mixture background model.
In another embodiment, the first determining unit determines that the original image matches the gaussian mixture background model, and includes pixel values
Defining the distance between the mean value of the ith Gaussian distribution in the Gaussian mixture background model and the standard deviation of the mean value of the ith Gaussian distribution to be less than 2.5 times of the standard deviation of the mean value of the ith Gaussian distribution in the Gaussian mixture background model
Matching; namely, it is
。
In another embodiment, after the second determining unit determines that the volume is greater than the first threshold, the second determining unit calculates a staying time of the foreground object, and determines that the foreground object is an intrusion if the staying time is greater than the second threshold.
In a further aspect, a computer-readable storage medium is proposed, on which a computer program is stored, which, when executed, implements the method for railway foreign object intrusion monitoring described in any of the above method embodiments.
That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
FIG. 4 is a block diagram illustrating a method for a computer device 40 according to an example embodiment. Referring to fig. 4, the apparatus 40 includes a processor 401, and the number of the processors may be set to one or more as necessary. The device 40 also includes a memory 402 for storing instructions, such as an application program, that are executable by the processor 401. The number of the memories can be set to one or more according to requirements. Which may store one or more application programs. The processor 401 is configured to execute instructions to perform the above-described method.
The memory 402 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store a list of options, etc. Further, the memory 402 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, memory 402 may optionally include memory located remotely from processor 401, which may be connected to an external device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
As will be appreciated by one skilled in the art, the embodiments herein may be provided as a method, apparatus (device), or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied in the medium. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, including, but not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer, and the like. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices) and computer program products according to embodiments herein. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments herein have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following appended claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of this disclosure.
It will be apparent to those skilled in the art that various changes and modifications may be made herein without departing from the spirit and scope thereof. Thus, it is intended that such changes and modifications be included herein, provided they come within the scope of the appended claims and their equivalents.
Claims (10)
1. A railway foreign body intrusion monitoring method is characterized by comprising the following steps:
reading an image of a video sequence, delimiting a detection area in the image;
judging whether the image is matched with a Gaussian mixed background model or not, and if not, extracting a foreground object of the image;
calculating the volume of a foreground object in the detection area;
judging whether the volume is larger than a first threshold value or not, and if so, judging that the foreign body is invaded;
and marking the position and the volume of the invasive foreign body.
2. The method for monitoring the invasion of foreign matters into the railway according to claim 1, wherein before judging whether the image is matched with a Gaussian mixture background model, the Gaussian mixture background model is modeled; each pixel of the background image is respectively modeled by a Gaussian mixture model formed by K Gaussian distributions, and the modeling of the Gaussian mixture background model specifically comprises the following steps:
wherein,
representing the value of the pixel j at the time t, if the pixel j is an RGB three-channel
In the form of a vector, the vector,
，
an estimate of the weight coefficients representing the ith gaussian distribution in the mixed gaussian background model at time t,
mean vectors representing the ith Gaussian distribution in the mixed Gaussian background model at the time t;
and (2) representing the covariance matrix of the ith Gaussian distribution in the mixed Gaussian background model at the moment t, and eta representing the probability density function of the Gaussian distribution.
3. The method of claim 2, wherein said determining that said image matches a Gaussian mixture background model comprises pixel values
Defining the distance between the mean value of the ith Gaussian distribution in the Gaussian mixture background model and the standard deviation of the mean value of the ith Gaussian distribution to be less than 2.5 times of the standard deviation of the mean value of the ith Gaussian distribution in the Gaussian mixture background model
Matching; namely, it is
。
4. The method for monitoring invasion of foreign matters into railways according to claim 3, wherein after the volume is judged to be larger than a first threshold value, the staying time of the foreground object is calculated, and if the staying time is larger than a second threshold value, the invasion of foreign matters is judged.
5. A railway foreign object intrusion monitoring system, comprising:
a detection unit for reading an image of a video sequence, defining a detection area in said image;
the first judgment unit is used for judging whether the image is matched with the Gaussian mixture background model or not, and if not, extracting a foreground object of the image;
a calculation unit for calculating the volume of a foreground object within the detection area;
the second judgment unit is used for judging whether the volume is larger than the first threshold value or not, and if so, judging that the foreign body is invaded;
and the marking unit is used for marking the position and the volume of the invasion foreign matter.
6. The system for monitoring intrusion of foreign matter into a railway according to claim 5, wherein the first judging unit models the gaussian mixture background model before judging whether the image is matched with the gaussian mixture background model; each pixel of the background image is respectively modeled by a Gaussian mixture model formed by K Gaussian distributions, and the modeling of the Gaussian mixture background model specifically comprises the following steps:
wherein,
representing the value of the pixel j at the time t, if the pixel j is an RGB three-channel
In the form of a vector, the vector,
，
an estimate of the weight coefficients representing the ith gaussian distribution in the gaussian background model at time t,
the mean vector of the ith Gaussian distribution in the mixed Gaussian background model at the moment t is represented;
and (2) representing the covariance matrix of the ith Gaussian distribution in the mixed Gaussian background model at the moment t, and eta representing the probability density function of the Gaussian distribution.
7. The system of claim 6, wherein said image is determined to match a Gaussian mixture background model comprising pixel values
Defining the distance between the mean value of the ith Gaussian distribution in the Gaussian mixture background model and the standard deviation of the mean value of the ith Gaussian distribution to be less than 2.5 times of the standard deviation of the mean value of the ith Gaussian distribution in the Gaussian mixture background model
Matching; namely, it is
。
8. The system for monitoring invasion of foreign matters into railways according to claim 7, wherein the second determination unit calculates the staying time of the foreground object after determining that the volume is larger than a first threshold value, and determines to invade foreign matters if the staying time is larger than a second threshold value.
9. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed, implements the steps of the method according to any one of claims 1-4.
10. A computer arrangement comprising a processor, a memory and a computer program stored on the memory, characterized in that the steps of the method according to any of claims 1-4 are implemented when the computer program is executed by the processor.
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