CN111311637A - Alarm event processing method and device, storage medium and electronic device - Google Patents
Alarm event processing method and device, storage medium and electronic device Download PDFInfo
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Abstract
The invention provides a method and a device for processing an alarm event, a storage medium and an electronic device, wherein the method comprises the following steps: acquiring a plurality of pictures of a plurality of target objects in a monitoring area through an image acquisition device, wherein a unique identifier is set for each target object in the plurality of target objects; analyzing the pictures to predict action tracks and action speeds of the target objects to obtain a prediction result; and triggering an alarm event under the condition that the prediction result indicates that the deviation of the motion tracks of part of the target objects exceeds a first threshold value or the action speed of the target objects exceeds a second threshold value.
Description
Technical Field
The invention relates to the field of communication, in particular to a method and a device for processing an alarm event, a storage medium and an electronic device.
Background
When the live pigs are unloaded to the swinery from the live pig transport truck, if the situation that the pigs only run in the reverse direction occurs in the swinery, the swinery can be disordered and trampled, and unnecessary loss is caused. The driving can only be monitored and driven in real time manually. Because the number of live pigs in a pig farm is large, and a small amount of manpower cannot timely take care of all the swinery, an unmanned method is needed to assist in timely finding the condition of the retrograde motion of the live pigs. The existing method is to manually monitor the herd to see if a live pig retrograde motion has occurred. However, in some cases, pigs are few, and it is difficult to find the retrograde motion behavior of live pigs in time. And the field of vision of people is limited, and the reverse running of piglets is difficult to find.
Aiming at the problem that the retrograde motion condition of the target object cannot be accurately monitored when a few workers exist in the related art, an effective technical scheme is not provided yet.
Disclosure of Invention
The embodiment of the invention provides a method and a device for processing an alarm event, a storage medium and an electronic device, which are used for at least solving the problem that the retrograde motion condition of a target object cannot be accurately monitored when fewer workers exist in the related art.
According to an embodiment of the present invention, a method for processing an alarm event is provided, including: acquiring a plurality of pictures of a plurality of target objects in a monitoring area through an image acquisition device, wherein a unique identifier is set for each target object in the plurality of target objects; analyzing the pictures to predict action tracks and action speeds of the target objects to obtain a prediction result; and triggering an alarm event when the prediction result indicates that the deviation of the motion tracks of part of the target objects exceeds a first threshold value or the action speed of the target objects exceeds a second threshold value.
Optionally, analyzing the multiple pictures includes: processing the plurality of pictures through a Kalman filter to obtain the position states of the target objects at the next moment corresponding to the plurality of target objects; and determining the track data respectively corresponding to the plurality of objects according to the position states.
Optionally, predicting the action tracks and the action speeds of a plurality of target objects to obtain a prediction result, including: inputting trajectory data into a vector machine model to determine action trajectories of a plurality of target objects; and determining the action speed of the target object by performing regression fitting processing on the trajectory data.
Optionally, the deviation of the motion trajectories of some target objects in the target objects exceeds a first threshold, including: the action tracks of the partial target objects are opposite to the action tracks of other target objects, wherein the other target objects are other target objects except the partial target objects in the plurality of target objects.
According to another embodiment of the present invention, there is provided an apparatus for processing an alarm event, including: the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring a plurality of pictures of a plurality of target objects in a monitoring area through an image acquisition device, and unique identification is set for each target object in the plurality of target objects; the prediction module is used for analyzing the pictures to predict the action tracks and the action speeds of the target objects to obtain a prediction result; and the triggering module is used for triggering an alarm event under the condition that the prediction result indicates that the deviation of the motion trail of part of the target objects exceeds a first threshold value or the action speed of the target objects exceeds a second threshold value.
Optionally, the prediction module is further configured to process the multiple pictures through a kalman filter to obtain the position states of the target objects at the next time corresponding to the multiple target objects; and determining the track data respectively corresponding to the plurality of objects according to the position states.
Optionally, the prediction module is further configured to input trajectory data into the vector machine model to determine action trajectories of the plurality of target objects; and determining the action speed of the target object by performing regression fitting processing on the trajectory data.
Optionally, the apparatus further comprises: a determination module, wherein the determination module is configured to determine that a deviation of motion trajectories of a portion of the target objects exceeds a first threshold by: the action tracks of the partial target objects are opposite to the action tracks of other target objects, wherein the other target objects are other target objects except the partial target objects in the plurality of target objects.
According to another embodiment of the present invention, there is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
According to the invention, a plurality of pictures of a plurality of target objects in a monitoring area are obtained through an image acquisition device, wherein a unique identifier is set for each target object in the plurality of target objects; analyzing the pictures to predict action tracks and action speeds of the target objects to obtain a prediction result; and triggering an alarm event under the condition that the prediction result indicates that the deviation of the motion tracks of part of the target objects exceeds a first threshold value or the action speed of the target objects exceeds a second threshold value.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a block diagram of a hardware configuration of a computer terminal of a method for processing an alarm event according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method of handling alarm events according to an embodiment of the present invention;
FIG. 3 is a flow chart of a pig retrograde detection method based on a multi-objective tracking algorithm according to an alternative embodiment of the present invention;
fig. 4 is a block diagram of a device for processing an alarm event according to an embodiment of the present invention.
Detailed Description
The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The report sending method provided by the embodiment of the application can be executed in a mobile terminal, a computer terminal or a similar operation device. Taking the example of running on a computer terminal, fig. 1 is a hardware structure block diagram of a computer terminal of a method for processing an alarm event according to an embodiment of the present invention. As shown in fig. 1, the computer terminal may include one or more (only one shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, and optionally, a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the computer terminal. For example, the computer terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration with equivalent functionality to that shown in FIG. 1 or with more functionality than that shown in FIG. 1.
The memory 104 may be used to store a computer program, for example, a software program and a module of application software, such as a computer program corresponding to the alarm event processing method in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, so as to implement the above-mentioned report sending method. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to a computer terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
In this embodiment, a method for processing an alarm event running on a computer terminal is provided, and fig. 2 is a flowchart of a method for processing an alarm event according to an embodiment of the present invention, as shown in fig. 2, the flowchart includes the following steps:
step S202, acquiring a plurality of pictures of a plurality of target objects in a monitoring area through an image acquisition device, wherein a unique identifier is set for each target object in the plurality of target objects;
step S204, analyzing the plurality of pictures to predict the action tracks and the action speeds of the plurality of target objects to obtain a prediction result;
step S206, when the prediction result indicates that the deviation of the motion tracks of part of the target objects exceeds a first threshold value, or the action speed of the target objects exceeds a second threshold value, an alarm event is triggered.
Through the steps, a plurality of pictures of a plurality of target objects in a monitoring area are obtained through an image acquisition device, wherein a unique identifier is set for each target object in the plurality of target objects; analyzing the pictures to predict action tracks and action speeds of the target objects to obtain a prediction result; and triggering an alarm event under the condition that the prediction result indicates that the deviation of the motion tracks of part of the target objects exceeds a first threshold value or the action speed of the target objects exceeds a second threshold value.
Optionally, analyzing the multiple pictures includes: processing the plurality of pictures through a Kalman filter to obtain the position states of the target objects at the next moment corresponding to the plurality of target objects; determining the track data corresponding to the plurality of objects respectively according to the position states, analyzing a plurality of pictures of a plurality of target objects in the monitoring area obtained by the image acquisition device, processing the plurality of pictures through a Kalman filter, and obtaining the track data corresponding to the plurality of targets, for example, processing the monitoring picture of the running of the pig (equivalent to the target object) in the monitoring area through the Kalman filter by the image acquisition device, determining the position state of each pig, and tracking the behavior track of each pig.
Optionally, predicting the action tracks and the action speeds of a plurality of target objects to obtain a prediction result, including: inputting trajectory data into a vector machine model to determine action trajectories of a plurality of target objects; and determining the action speed of the target object by performing regression fitting processing on the trajectory data.
Optionally, the deviation of the motion trajectories of some target objects in the target objects exceeds a first threshold, including: when the deviation of the motion trajectories of the target objects exceeds a first threshold, which indicates that the target objects do not move according to the predicted motion direction, the motion trajectories of the target objects are opposite to the predicted motion direction. For example, pigs only run during their journey, escape surveillance and have a deviation in trajectory.
In order to better understand the report sending process, the following describes the above procedure with reference to an alternative embodiment, but is not intended to limit the technical solution of the embodiment of the present invention, and includes the following steps:
according to the optional embodiment of the invention, the monitor is arranged at a relatively higher position, so that the problem that the piglet cannot be seen due to being shielded is effectively avoided. A large number of pig targets can be identified simultaneously through multi-target identification and tracking, and the condition that retrogressive pigs are missed due to monitoring caused by insufficient manual labor is avoided. FIG. 3 is a flow chart of a pig retrograde motion detection method based on a multi-target tracking algorithm, comprising the following specific steps:
firstly, a pig reverse running detection method based on a multi-target tracking algorithm collects a running monitoring picture of a pig (equivalent to a target object) in a monitoring area through an image acquisition device.
And step two, the pig reverse running detection service tracks the behavior track of each pig by detecting the position of each pig in the monitoring picture and correspondingly endowing each pig with a unique number (which is equivalent to setting a unique identifier for each target object in a plurality of target objects).
Specifically, the state information such as the next motion position of the pig is predicted through a Kalman filter, a high-dimensional pig image feature vector (3136 dimension) extracted based on Resnet50 is connected, the feature vector is projected to a feature space, the similarity of each pig is obtained through triplet loss, the matching is successful when the similarity is higher than a set threshold value, the numbering is carried out again when the similarity is lower than the set threshold value, the behavior track of each pig is tracked, alternatively, the Kalman filter can predict the position state most possibly appearing in the pig at the next moment by inputting the state of the motion position of the pig at the previous moment, the output of the Kalman filter is combined with the image characteristic information to form a model for tracking the pig, the method can be understood as an object tracking algorithm, and finally the pig appearing at the current moment is only one pig in the monitoring picture at the last moment, so that the position state of the same pig at each moment is obtained.
Extracting the track data of each target object at regular intervals, performing standardized scalar processing, inputting a support vector machine model of a Gaussian kernel for prediction, identifying the motion direction of each track by a classification method, and because the motion tracks of each pig are not completely the same, the embodiment of the invention further identifies the trend of the motion track of the pig by an algorithm, fitting the speed by a regression method (the distance is the same and can be predicted, and the precision is higher than that of a linear model, but the pig is not required to be used in the application scene, specifically, using the position information of each moment of the pig, using the information of the Euclidean distance of each position as input, establishing a regression model by a Multi-layer Perceptron (Multi-layer Perceptron), and outputting the moving speed of the corresponding pig by the regression model). The model has nonlinear fitting capability and is more suitable for processing the pictures acquired by the monitor.
And step four, when the algorithm counts the predicted result, the pig in the monitoring picture only has various movement directions and speeds with great deviation, and the alarm information is immediately sent to field workers, and the sending mode of the alarm information is not limited by the invention.
Through the optional embodiment, the pig reverse detection method based on the multi-target tracking algorithm firstly acquires the pig walking picture through the image acquisition device, and the pig reverse detection service can detect the position of each pig in the picture, assign a unique number to each pig and track the behavior track of each pig. And then, identifying the motion direction of each track by using a support vector machine for each target track data at regular intervals, and immediately sending alarm information to field workers when various motion tracks with great deviation appear in a monitoring picture. Furthermore, the characteristics of the target object extracted by the embodiment of the invention are richer, not only are the color and edge characteristics limited, but also all image characteristics of the pig are included, so that the accuracy is higher, a segmentation algorithm is not used, the speed is higher, and the calculation amount is smaller.
According to the optional embodiment of the invention, the multi-target tracking algorithm is used, the Kalman filter and the Resnet50 are used for extracting the image features, and each detected pig is matched through the triplet loss, because the utilized image features far exceed the color and edge features, the precision is better than that of the pig tracking method based on the shortest Euclidean distance matching. In addition, the embodiment of the invention predicts whether the reverse movement of the pig exists or not by classifying all the tracks, and automatically identifies the reverse movement of the pig not for acquiring information such as displacement and the like, thereby reducing the manual workload and improving the accuracy of detecting the reverse movement of the pig.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
In this embodiment, a training apparatus for a speech recognition model is further provided, and the apparatus is used to implement the foregoing embodiments and preferred embodiments, which have already been described and are not described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 4 is a block diagram of an apparatus for processing an alarm event according to an embodiment of the present invention, as shown in fig. 4, the apparatus includes:
the acquiring module 42 is configured to acquire, through an image acquisition device, a plurality of pictures of a plurality of target objects in a monitored area, where a unique identifier is set for each of the plurality of target objects;
the prediction module 44 is configured to analyze the multiple pictures to predict action tracks and action speeds of multiple target objects, so as to obtain a prediction result;
a triggering module 46, configured to trigger an alarm event if the prediction result indicates that a deviation of motion trajectories of a part of the target objects exceeds a first threshold, or that an action speed of the target object exceeds a second threshold.
By the device, a plurality of pictures of a plurality of target objects in a monitoring area are obtained through an image acquisition device, wherein a unique identifier is set for each target object in the plurality of target objects; analyzing the pictures to predict action tracks and action speeds of the target objects to obtain a prediction result; and triggering an alarm event under the condition that the prediction result indicates that the deviation of the motion tracks of part of the target objects exceeds a first threshold value or the action speed of the target objects exceeds a second threshold value.
Optionally, the prediction module is further configured to process the multiple pictures through a kalman filter to obtain the position states of the target objects at the next time corresponding to the multiple target objects; determining the track data corresponding to the plurality of objects respectively according to the position states, analyzing a plurality of pictures of a plurality of target objects in the monitoring area obtained by the image acquisition device, processing the plurality of pictures through a Kalman filter, and obtaining the track data corresponding to the plurality of targets, for example, processing the monitoring picture of the running of the pig (equivalent to the target object) in the monitoring area through the Kalman filter by the image acquisition device, determining the position state of each pig, and tracking the behavior track of each pig.
Optionally, the prediction module is further configured to input trajectory data into the vector machine model to determine action trajectories of the plurality of target objects; and determining the action speed of the target object by performing regression fitting processing on the trajectory data.
Optionally, the apparatus further comprises: a determination module, wherein the determination module is configured to determine that a deviation of motion trajectories of a portion of the target objects exceeds a first threshold by: when the deviation of the motion trajectories of the target objects exceeds a first threshold, which indicates that the target objects do not move according to the predicted motion direction, the motion trajectories of the target objects are opposite to the predicted motion direction. For example, pigs only run during their journey, escape surveillance and have a deviation in trajectory.
An embodiment of the present invention further provides a storage medium including a stored program, wherein the program executes any one of the methods described above.
Alternatively, in the present embodiment, the storage medium may be configured to store program codes for performing the following steps:
s1, acquiring a plurality of pictures of a plurality of target objects in a monitoring area through an image acquisition device, wherein a unique identifier is set for each target object in the plurality of target objects;
s2, analyzing the pictures to predict the action tracks and the action speeds of the target objects to obtain a prediction result;
s3, an alarm event is triggered when the prediction result indicates that the deviation of the motion trail of part of the target objects exceeds a first threshold value or the action speed of the target objects exceeds a second threshold value.
An embodiment of the present invention further provides a storage medium including a stored program, wherein the program executes any one of the methods described above.
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, acquiring a plurality of pictures of a plurality of target objects in a monitoring area through an image acquisition device, wherein a unique identifier is set for each target object in the plurality of target objects;
s2, analyzing the pictures to predict the action tracks and the action speeds of the target objects to obtain a prediction result;
s3, an alarm event is triggered when the prediction result indicates that the deviation of the motion trail of part of the target objects exceeds a first threshold value or the action speed of the target objects exceeds a second threshold value.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only exemplary 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 principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for processing an alarm event is characterized by comprising the following steps:
acquiring a plurality of pictures of a plurality of target objects in a monitoring area through an image acquisition device, wherein a unique identifier is set for each target object in the plurality of target objects;
analyzing the pictures to predict action tracks and action speeds of the target objects to obtain a prediction result;
and triggering an alarm event when the prediction result indicates that the deviation of the motion tracks of part of the target objects exceeds a first threshold value or the action speed of the target objects exceeds a second threshold value.
2. The method of claim 1, wherein analyzing the plurality of pictures comprises:
processing the plurality of pictures through a Kalman filter to obtain the position states of the target objects at the next moment corresponding to the plurality of target objects;
and determining the track data respectively corresponding to the plurality of objects according to the position states.
3. The method of claim 2, wherein predicting the action tracks and action speeds of a plurality of target objects to obtain a prediction result comprises:
inputting the trajectory data into a vector machine model to determine trajectories of the plurality of target objects; and
and determining the action speed of the target object by performing regression fitting processing on the trajectory data.
4. The method of claim 1, wherein the deviation of the motion trajectories of the portion of the target objects exceeds a first threshold value, comprising:
the action tracks of the partial target objects are opposite to the action tracks of other target objects, wherein the other target objects are other target objects except for the partial target objects in the plurality of target objects.
5. An alert event processing apparatus, comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring a plurality of pictures of a plurality of target objects in a monitoring area through an image acquisition device, and unique identification is set for each target object in the plurality of target objects;
the prediction module is used for analyzing the pictures to predict the action tracks and the action speeds of the target objects to obtain a prediction result;
and the triggering module is used for triggering an alarm event under the condition that the prediction result indicates that the deviation of the motion trail of part of the target objects exceeds a first threshold value or the action speed of the target objects exceeds a second threshold value.
6. The apparatus according to claim 5, wherein the prediction module is further configured to process the plurality of pictures through a kalman filter to obtain the position states of the target objects at the next time corresponding to the plurality of target objects; and determining the track data respectively corresponding to the plurality of objects according to the position states.
7. The apparatus of claim 6, wherein the prediction module is further configured to input the trajectory data into a vector machine model to determine trajectories of actions of the plurality of target objects; and determining the action speed of the target object by performing regression fitting processing on the trajectory data.
8. The apparatus of claim 5, further comprising: a determination module, wherein the determination module is configured to determine that a deviation of motion trajectories of a portion of the target objects exceeds a first threshold by: the action tracks of the partial target objects are opposite to the action tracks of other target objects, wherein the other target objects are other target objects except for the partial target objects in the plurality of target objects.
9. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to carry out the method of any one of claims 1 to 4 when executed.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 4.
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| CN202010084940.9A CN111311637A (en) | 2020-02-10 | 2020-02-10 | Alarm event processing method and device, storage medium and electronic device |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115759033A (en) * | 2022-11-21 | 2023-03-07 | 厦门海兰寰宇海洋信息科技有限公司 | Method, device and equipment for processing track data |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115759033A (en) * | 2022-11-21 | 2023-03-07 | 厦门海兰寰宇海洋信息科技有限公司 | Method, device and equipment for processing track data |
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