CN110351523B - Building video monitoring system and video monitoring adjustment method - Google Patents
Building video monitoring system and video monitoring adjustment method Download PDFInfo
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- CN110351523B CN110351523B CN201910658427.3A CN201910658427A CN110351523B CN 110351523 B CN110351523 B CN 110351523B CN 201910658427 A CN201910658427 A CN 201910658427A CN 110351523 B CN110351523 B CN 110351523B
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 30
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/76—Television signal recording
- H04N5/78—Television signal recording using magnetic recording
- H04N5/781—Television signal recording using magnetic recording on disks or drums
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
Abstract
The invention belongs to the field of security and video monitoring, and relates to a building video monitoring system and a video monitoring adjusting method. The system comprises an image data acquisition module, a processing control module, an Ethernet controller and a cloud server; the method comprises the following steps that a microprocessor is used as a processing control module, a camera is used as a video image data acquisition module, and the microprocessor is respectively interconnected with the image data acquisition module, the processing control module, a memory, a hard disk and an Ethernet controller; the Ethernet controller is connected with the cloud server through the Internet. And a linux operating system and drivers required by a camera and an Ethernet controller are transplanted in the microprocessor. The invention predicts the possibility of the criminal behavior in the current time period according to the criminal behavior historical data records in and near the buildings, changes the frame rate of the stored images according to the possibility, adjusts the size of the video file and achieves the aim of saving the storage space.
Description
Technical Field
The invention belongs to the field of security and video monitoring, and relates to a building video monitoring system and a video monitoring adjusting method.
Background
At present, high-definition video monitoring brings about great improvement of image quality and also brings about increase of cost on storage equipment. Saving memory space is an important method to reduce cost. According to data statistics, the occurrence of criminal behaviors at different time nodes (month, week and hour) is regular. As shown in fig. 1 and fig. 2, according to the historical data records of the criminal behavior occurring in and near the building, the possibility of the criminal behavior occurring in the current time period can be predicted, and according to the possibility of the criminal behavior, the frame rate of the stored image is changed, the size of the video file is adjusted, and the purpose of saving the storage space can be achieved.
Disclosure of Invention
In order to solve the technical problems, the invention provides a building video monitoring system and a video monitoring adjustment method, which predict the possibility of the occurrence of the criminal behavior in the current time period according to the historical data record of the criminal behavior occurring in the building and the vicinity of the building, change the frame rate of the stored image according to the possibility, adjust the size of the video file and achieve the purpose of saving the storage space.
The technical scheme of the invention is as follows:
a building video monitoring system comprises an image data acquisition module, a processing control module, an Ethernet controller and a cloud server; a microprocessor is adopted as a processing control module, and further the microprocessor is an ARM; the camera is used as a video image data acquisition module, and the microprocessor is respectively interconnected with the image data acquisition module, the memory, the hard disk and the Ethernet controller; the Ethernet controller is connected with the cloud server through the Internet.
Furthermore, a linux operating system and drivers required by a camera and an Ethernet controller are transplanted in the microprocessor.
V4L2(video 4Linux 2) is in a Linux kernel and relates to switch control of video equipment, and video image information acquisition and processing. The video and audio equipment can be controlled through the system API provided by V4L2 after the corresponding equipment drivers are installed. The process of acquiring the image is operationally, i.e., the process of calling the various functions of V4L 2.
The video monitoring adjustment method adopting the building video monitoring system comprises the following steps:
the method comprises the steps of firstly, selecting typical data according to criminal behavior historical data records appearing in buildings and nearby buildings, and updating the data on a cloud server according to new criminal behaviors and new data.
The historical data records are classified according to months, weeks and hours, and the records of criminal behaviors in the classification time period are 1 and the records of criminal behaviors in the classification time period are 0;
secondly, setting the month, week and hour corresponding to the current time as m, w and h, calculating the distance between the current time and each group of data, and setting the month, week and hour corresponding to the first group of data as m1, w1 and h 1;
2.1 ordering m1 with m, if m > m1, keeping the value unchanged; if m < m1, exchanging the values of m, m1, so that m > m 1; for w and w1, h and h1 are also ordered in this manner.
2.2 order of
Xm1＝min(m-m1,12+m1-m)
Xw1＝min(w-w1,7+w1-w)
Xh1＝min(h-h1,24+h1-h)
Third, the distance d between the current time vector and the first set of time vectors1Comprises the following steps:
further, the distances between the flow number of the personnel and the police strength number in the current time period and the first group of vectors of the personnel and the police strength number in the current time period can be added;
fourthly, calculating the distance di between the current time and the time corresponding to each group of data;
fifthly, sorting according to the increasing order of the distances di; and selecting K points with the minimum distance from the current time.
And sixthly, returning the category with the highest occurrence frequency of the previous K points as the prediction classification of the existence of the criminal behavior corresponding to the current time.
And seventhly, predicting according to the existence of the criminal behaviors in the current time period: when the criminal behavior is predicted, the frame frequency of the video monitoring is unchanged; and when no criminal behavior is predicted, reducing the frame rate of video monitoring and saving the storage space.
The method for reducing the frame rate of video monitoring comprises the following steps:
and calling a function (such as embedded linux is a system function ioctl) to acquire a frame of image. If the current time quantum predicts that the criminal behavior exists, all the images read from the memory are transferred to the hard disk; if the current time period is predicted to be non-criminal, the images read from the memory are discarded according to a set ratio, and the purpose of reducing the frame rate of video monitoring is achieved. The program that completes step 7 runs on the local microprocessor.
The invention has the following beneficial effects;
the invention predicts the possibility of the criminal behavior in the current time period according to the criminal behavior historical data records in and near the buildings, changes the frame rate of the stored images according to the possibility, adjusts the size of the video file and achieves the aim of saving the storage space.
Drawings
Fig. 1 is a statistical graph of crime behavior at different time nodes (months).
Fig. 2 is a statistical graph of criminal behaviors at different time nodes (time).
Fig. 3 is a schematic structural diagram of a building video monitoring system.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
A video monitoring adjustment method of a building video monitoring system comprises the following steps:
firstly, selecting typical data according to criminal behavior historical data records appearing at and near buildings, as follows:
the data is updated on the cloud server according to the appearance of new data of new criminal behaviors.
Secondly, setting the month, week and hour corresponding to the current time as m, w and h, calculating the distance between the current time and each group of data, and setting the month, week and hour corresponding to the first group of data as m1, w1 and h 1;
2.1 ordering m1 with m, if m > m1, keeping the value unchanged; if m < m1, exchanging the values of m, m1, so that m > m 1; for w and w1, h and h1 are also ordered in this manner.
2.2 order of
Xm1＝min(m-m1,12+m1-m)
Xw1＝min(w-w1,7+w1-w)
Xh1＝min(h-h1,24+h1-h)
Third, the distance d between the current time vector and the first set of time vectors1Comprises the following steps:
fourthly, calculating the distance di between the current time and the time corresponding to each group of data;
fifthly, sorting according to the increasing order of the distances di; and 9 points with the minimum distance from the current time are selected.
And sixthly, returning the category with the highest frequency of occurrence at the first 9 points as the prediction classification of whether the criminal behavior corresponding to the current time exists, and when k is 9 and 6(6>5) criminal behaviors exist in the 9 points, predicting the current time period as the criminal behavior.
The program runs on the cloud server, and after the program obtains the prediction of the criminal behavior in the current time period, the prediction result is transmitted to the local microprocessor through the network protocol.
And seventhly, predicting according to the existence of the criminal behaviors in the current time period: when the criminal behavior is predicted, the frame frequency of the video monitoring is unchanged; and when no criminal behavior is predicted, reducing the frame rate of video monitoring and saving the storage space.
The method for reducing the frame rate of video monitoring comprises the following steps:
and acquiring a frame of image by adopting the embedded linux as a system function ioctl. If the current time quantum predicts that the criminal behavior exists, all the images read from the memory are transferred to the hard disk; if the current time period is predicted to be non-criminal, the image read from the memory is reserved for 60% and discarded for 40% according to the set proportion, the purpose of reducing the frame rate of video monitoring is achieved, and the program in the step 7 is completed and runs in a local microprocessor.
Claims (5)
1. A video monitoring and adjusting method is characterized by comprising the following steps:
firstly, selecting typical data according to criminal behavior historical data records appearing in buildings and nearby buildings, and updating according to the appearance of new criminal behavior data;
the historical data records are classified according to months, weeks and hours, and the records of criminal behaviors in the classification time period are 1 and the records of criminal behaviors in the classification time period are 0;
secondly, setting the month, week and hour corresponding to the current time as m, w and h, calculating the distance between the current time and each group of data, and setting the month, week and hour corresponding to the first group of data as m1, w1 and h 1;
2.1 ordering m1 with m, if m > m1, keeping the value unchanged; if m < m1, exchanging the values of m, m1, so that m > m 1; the pairs w and w1, h and h1 are also ordered in this manner;
2.2 order of
Xm1＝min(m-m1,12+m1-m)
Xw1＝min(w-w1,7+w1-w)
Xh1＝min(h-h1,24+h1-h)
Third, the distance d between the current time vector and the first set of time vectors1Comprises the following steps:
fourthly, calculating the distance di between the current time and the time corresponding to each group of data;
fifthly, sorting according to the increasing order of the distances di; selecting K points with the minimum distance from the current time;
sixthly, returning the category with the highest occurrence frequency of the previous K points as the prediction classification of whether the criminal behavior corresponding to the current time exists or not;
and seventhly, predicting according to the existence of the criminal behaviors in the current time period: when the criminal behavior is predicted, the frame frequency of the video monitoring is unchanged; and when no criminal behavior is predicted, reducing the frame rate of video monitoring and saving the storage space.
2. The video surveillance adjustment method according to claim 1, wherein in the seventh step, the method for reducing the frame rate of video surveillance comprises: calling a function to obtain a frame of image, and if the current time period is predicted to have criminal behavior, transferring all the images read from the memory to a hard disk; and if the current time period is predicted to be non-criminal, discarding the images read from the memory according to a set ratio.
3. The video surveillance adjustment method according to claim 1 or 2, wherein the video surveillance adjustment method is implemented by using a building video surveillance system, and the building video surveillance system comprises an image data acquisition module, a processing control module, an ethernet controller and a cloud server; a microprocessor is used as a processing control module, a camera is used as a video image data acquisition module, and the microprocessor is respectively interconnected with the image data acquisition module, a memory, a hard disk and an Ethernet controller; the Ethernet controller is connected with the cloud server through the Internet.
4. The video surveillance adjustment method according to claim 3, wherein the building video surveillance system is provided with a linux operating system and drivers required for the transplantation of the camera and the Ethernet controller in the microprocessor.
5. The video surveillance adjustment method of claim 3, wherein an ARM is adopted as a microprocessor in the building video surveillance system.
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