CN112291538A - Video monitoring data storage method and device - Google Patents
Video monitoring data storage method and device Download PDFInfo
- Publication number
- CN112291538A CN112291538A CN202011598302.5A CN202011598302A CN112291538A CN 112291538 A CN112291538 A CN 112291538A CN 202011598302 A CN202011598302 A CN 202011598302A CN 112291538 A CN112291538 A CN 112291538A
- Authority
- CN
- China
- Prior art keywords
- video
- current frame
- video monitoring
- monitoring data
- storage area
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/76—Television signal recording
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Closed-Circuit Television Systems (AREA)
- Compression Or Coding Systems Of Tv Signals (AREA)
Abstract
The application provides a method and a device for storing video monitoring data. The method comprises the steps that target behavior detection is carried out on video monitoring data, if abnormal behaviors exist in the video monitoring data, video monitoring data segments corresponding to the abnormal behaviors are stored in a first storage area of storage equipment in a lossless coding mode, and the video monitoring data are stored in a second storage area of the storage equipment in a compressed coding mode; and if the abnormal behavior is not detected in the video monitoring data, storing the video monitoring data to a second storage area of the storage device in a compressed coding mode. Therefore, the video monitoring data corresponding to the abnormal behaviors can be clearly stored, and the video monitoring data corresponding to the abnormal behaviors in a longer time period can be stored; the video monitoring data without abnormal behaviors are stored in a low resolution mode, and videos with longer storage periods can be stored.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for storing video monitoring data.
Background
The intelligent video signal processing technology is being widely applied to various aspects of daily life, such as intelligent video monitoring systems in the fields of industrial safety production, lane road safety and the like. As the application scenarios scale up, there are also more challenges to come up.
Compared with the existing traditional video monitoring, the intelligent video monitoring system has the advantages that abnormal behaviors in different scenes can be accurately and timely detected, and prompt and alarm are carried out, for example, in the field of lane road safety, the intelligent video monitoring system can detect abnormal behaviors such as overspeed and retrograde motion in real time.
The traditional video monitoring system can completely store videos within a period of time, but cannot provide an abnormal behavior alarming function in real time, and requires higher time cost for tracing back; due to the embedding of a target detection algorithm, the intelligent video monitoring system faces the challenges of high calculation pressure and high bandwidth requirement. No matter the traditional or intelligent system, under the requirement of high video resolution, the problem of short video storage period often exists due to the storage limit, especially for the video monitoring system deployed in the edge device.
Therefore, a method for storing video monitoring data is needed to solve the problem of short video period in the video monitoring system in the prior art.
Disclosure of Invention
The application provides a method and a device for storing video monitoring data, which can be used for solving the technical problem that the video period stored by a video monitoring system in the prior art is short.
In a first aspect, an embodiment of the present application provides a method for storing video monitoring data, where the method includes:
acquiring video monitoring data shot by a camera;
performing target behavior detection on the video monitoring data, if abnormal behaviors are detected in the video monitoring data, storing video monitoring data segments corresponding to the abnormal behaviors to a first storage area of a storage device in a lossless coding mode, and storing the video monitoring data to a second storage area of the storage device in a compressed coding mode;
wherein the first storage area and the second storage area jointly constitute the entire storage area of the storage device.
With reference to the first aspect, in an implementation manner of the first aspect, performing target behavior detection on the video monitoring data, and if it is detected that an abnormal behavior exists in the video monitoring data, storing a video monitoring data segment corresponding to the abnormal behavior in a lossless coding form to a first storage area of a storage device includes:
performing target behavior detection on the current frame video data in the video monitoring data, and if abnormal behavior is detected in the current frame video data, performing target behavior detection on the video data of N frames behind the current frame;
if no abnormal behavior is detected in the video data of the N frames after the current frame, storing a first video data segment from M frames before the current frame to N frames after the current frame in a lossless coding mode to a first storage area of the storage device;
wherein N and M are integers greater than or equal to 1.
With reference to the first aspect, in an implementation manner of the first aspect, the method further includes:
and taking the current frame as a storage identifier of the first video segment, and storing the current frame in a first storage area of the storage device.
With reference to the first aspect, in an implementation manner of the first aspect, the method further includes:
if the video data of the N frames behind the current frame is detected to have abnormal behaviors, performing target behavior detection on the video data of the 2N frames behind the current frame;
if no abnormal behavior is detected in the video data of the 2N frames following the current frame, storing a second video data segment from the M frames preceding the current frame to the 2N frames following the current frame in a lossless coding form to a first storage area of the storage device.
With reference to the first aspect, in an implementation manner of the first aspect, the method further includes:
determining a target frame when abnormal behaviors appear in the video data of the N frames after the current frame;
and determining a storage identifier of the second video data segment according to the current frame and the target frame, and storing the storage identifier into a second storage area of the storage device.
With reference to the first aspect, in an implementation manner of the first aspect, the method further includes:
and if the video monitoring data is not detected to have abnormal behaviors, storing the video monitoring data to a second storage area of the storage device in a compressed coding mode.
In a second aspect, an embodiment of the present application provides a storage apparatus for video surveillance data, where the apparatus includes:
the acquisition unit is used for acquiring video monitoring data shot by the camera;
the processing unit is used for detecting the target behaviors of the video monitoring data, storing video monitoring data segments corresponding to abnormal behaviors to a first storage area of a storage device in a lossless coding mode when the video monitoring data are detected to have abnormal behaviors, and storing the video monitoring data to a second storage area of the storage device in a compressed coding mode;
wherein the first storage area and the second storage area jointly constitute the entire storage area of the storage device.
With reference to the second aspect, in an implementable manner of the second aspect, the processing unit is specifically configured to:
performing target behavior detection on current frame video data in the video monitoring data, and performing target behavior detection on video data of N frames behind the current frame when abnormal behavior is detected in the current frame video data; when the abnormal behavior is not detected in the video data of the N frames after the current frame, storing a first video data segment from M frames before the current frame to N frames after the current frame into a first storage area of the storage device in a lossless coding mode;
wherein N and M are integers greater than or equal to 1.
With reference to the second aspect, in an implementable manner of the second aspect, the processing unit is further configured to:
and taking the current frame as a storage identifier of the first video segment, and storing the current frame in a first storage area of the storage device.
With reference to the second aspect, in an implementable manner of the second aspect, the processing unit is further configured to:
when detecting that abnormal behaviors exist in the video data of the N frames behind the current frame, carrying out target behavior detection on the video data of the 2N frames behind the current frame; when the abnormal behavior is not detected in the video data of the 2N frames after the current frame, storing a second video data segment from M frames before the current frame to the 2N frames after the current frame in a lossless coding mode to a first storage area of the storage device.
With reference to the second aspect, in an implementable manner of the second aspect, the processing unit is further configured to:
determining a target frame when abnormal behaviors appear in the video data of the N frames after the current frame; and determining a storage identifier of the second video data segment according to the current frame and the target frame, and storing the storage identifier to a second storage area of the storage device.
With reference to the second aspect, in an implementable manner of the second aspect, the processing unit is further configured to:
and when the video monitoring data is not detected to have abnormal behaviors, storing the video monitoring data to a second storage area of the storage device in a compression coding mode.
In the embodiment of the application, by detecting the target behaviors of the video monitoring data, if abnormal behaviors are detected in the video monitoring data, video monitoring data segments corresponding to the abnormal behaviors are stored in a first storage area of a storage device in a lossless coding mode, and the video monitoring data are stored in a second storage area of the storage device in a compressed coding mode; and if the abnormal behavior is not detected in the video monitoring data, storing the video monitoring data to a second storage area of the storage device in a compressed coding mode. By adopting the storage method, the key part in the monitoring video, namely the video monitoring data corresponding to the abnormal behavior can be clearly stored, the space occupied by each segment is small, the video monitoring data corresponding to the abnormal behavior in a longer time period can be stored, when the abnormal behavior needs to be traced, the efficiency is improved, and the video segments in a longer time period can be provided; and in the same storage space, the video monitoring data without abnormal behaviors are stored at low resolution, so that videos with longer storage periods can be stored.
Drawings
Fig. 1 is a schematic flowchart of a method for storing video monitoring data according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a storage device according to an embodiment of the present application;
fig. 3 is a schematic flowchart of a process corresponding to a method for detecting a target behavior of video monitoring data and determining whether an abnormal behavior exists in the embodiment of the present application;
FIG. 4 is a schematic diagram of a system architecture provided by an embodiment of the present application;
fig. 5 is a schematic structural diagram of a storage device for video surveillance data according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, a schematic flow chart corresponding to a video monitoring data storage method provided in an embodiment of the present application is exemplarily shown. As shown in fig. 1, the method specifically comprises the following steps:
and step S101, acquiring video monitoring data shot by a camera.
And S102, carrying out target behavior detection on the video monitoring data.
Step S103, judging whether abnormal behaviors exist in the video monitoring data or not, and executing step S104 if the abnormal behaviors exist in the video monitoring data; otherwise, step S105 is performed.
Step S104, storing the video monitoring data segment corresponding to the abnormal behavior to a first storage area of the storage device in a lossless coding mode, and storing the video monitoring data to a second storage area of the storage device in a compressed coding mode.
And step S105, storing the video monitoring data to a second storage area of the storage device in a compressed and encoded form.
In the embodiment of the application, by detecting the target behaviors of the video monitoring data, if abnormal behaviors are detected in the video monitoring data, video monitoring data segments corresponding to the abnormal behaviors are stored in a first storage area of a storage device in a lossless coding mode, and the video monitoring data are stored in a second storage area of the storage device in a compressed coding mode; and if the abnormal behavior is not detected in the video monitoring data, storing the video monitoring data to a second storage area of the storage device in a compressed coding mode. By adopting the storage method, the key part in the monitoring video, namely the video monitoring data corresponding to the abnormal behavior can be clearly stored, the space occupied by each segment is small, the video monitoring data corresponding to the abnormal behavior in a longer time period can be stored, when the abnormal behavior needs to be traced, the efficiency is improved, and the video segments in a longer time period can be provided; and in the same storage space, the video monitoring data without abnormal behaviors are stored at low resolution, so that videos with longer storage periods can be stored.
Specifically, in step S101, with the rapid development of national economy and information technology, and the rapid development of network technology, the application of the monitoring system in various industries is becoming more and more extensive, and the monitoring system is not applied in communication, traffic, security and other industries alone. It is gradually developing to other industries and the public. The video monitoring is carried out in the way of security video monitoring and vehicle-mounted video monitoring, and the video monitoring is almost everywhere visible in daily life.
Video monitoring can not leave the camera, and the camera can be divided into two categories, namely a digital camera and an analog camera. The digital camera can convert the analog video signal generated by the video acquisition equipment into a digital signal, and then store the digital signal in the computer. The video signal captured by the analog camera must be converted into a digital mode through a specific video capture card, and the digital mode can be converted into a computer for use after being compressed.
In the embodiment of the application, the camera in the traditional video monitoring system can be adopted to shoot video monitoring data.
The traditional video monitoring system can completely store videos in a period of time, but cannot provide an abnormal behavior alarm function in real time, and higher time cost is required for tracing.
Further, in the embodiment of the application, a camera in the intelligent video monitoring system can be used for shooting video monitoring data.
Compared with the existing traditional video monitoring, the intelligent video monitoring system has the advantages that abnormal behaviors in different scenes can be accurately and timely detected, and prompt and alarm are carried out, for example, in the field of lane road safety, the intelligent video monitoring system can detect abnormal behaviors such as overspeed and retrograde motion in real time.
In steps S102 to S105, the function of performing target behavior detection on the video monitoring data may be implemented by a target detection algorithm. Target detection algorithms can generally be divided into two broad categories: one is target detection based on a traditional image algorithm, and the other is target detection based on a deep learning algorithm.
It should be noted that, the specific method for detecting the target behavior may refer to a target detection method based on a traditional image algorithm in the prior art, or refer to a target detection method based on a deep learning algorithm, or refer to other target behavior detection methods, which are not described in detail herein.
In the embodiment of the present application, as shown in fig. 2, the storage device may be divided into two storage areas, which are a first storage area and a second storage area respectively. Wherein the first storage area and the second storage area jointly constitute the entire storage area of the storage device.
Further, the first storage area may be used for storing video data in a lossless encoded form, wherein the video data in a lossless encoded form is typically higher in resolution, i.e. the first storage area may be used for storing video data of high resolution. The second storage area may be used for storing video data in a compressed encoded form, wherein the compressed encoded form of video data is typically of lower resolution, i.e. the second storage area may be used for storing video data of lower resolution.
Further, the first storage area may occupy q% of the entire memory space of the storage device, and accordingly, the first storage area may occupy (1-q)%, where the specific value of q may be determined according to the video content, the occurrence frequency of the abnormal event, and the size of each segment.
It should be noted that, the second storage area adopts a ring cache policy, and when the storage space of the second storage area is full, the current video will overwrite the oldest stored video.
Thus, high resolution video data will occupy q% of the total memory space, and low resolution overall video will occupy (1-q)% of the total memory space. By the aid of the partition storage mode, key contents, namely video monitoring data corresponding to abnormal behaviors, can be clearly and definitely stored, abnormal behaviors in the video monitoring data can be conveniently searched and traced, and each segment occupies a small space and can store abnormal behavior segments within a long time period; meanwhile, the complete video monitoring data is stored by using lower resolution, the video storage period can be prolonged, and the whole video is convenient to view when the user wants to look up.
And detecting the target behaviors of the video monitoring data and judging whether abnormal behaviors exist or not, wherein various modes can be adopted in the specific implementation process.
A possible implementation manner is to refer to fig. 3, which exemplarily shows a flow diagram corresponding to a method for performing target behavior detection on video monitoring data and determining whether an abnormal behavior exists in the embodiment of the present application, and specifically includes the following steps:
step S301, performing target behavior detection on the current frame video data in the video monitoring data, judging whether abnormal behavior exists in the current frame video data, and executing step S302 if the abnormal behavior exists in the current frame video data; otherwise, step S309 is performed.
Step S302, performing target behavior detection on the video data of N frames after the current frame.
Step S303, judging whether abnormal behaviors exist in the video data of the N frames after the current frame, and executing step S304 if the abnormal behaviors exist in the video data of the N frames after the current frame; otherwise, step S308 is executed.
Step S304, performing target behavior detection on the video data of 2N frames after the current frame.
Step S305, judging whether abnormal behaviors exist in the video data of the 2N frame after the current frame, if abnormal behaviors exist in the video data of the 2N frame after the current frame, executing step S306; otherwise, step S307 is executed.
Step S306, continuing to perform target behavior detection on the video data of the 3N frame after the current frame until no abnormal behavior is detected in the video data of the 2N frame after the current frame.
In step S307, the second video data segment from the M frame before the current frame to the 2N frame after the current frame is stored in the first storage area of the storage device in a lossless coding form.
Step S308, storing a first video data segment from M frames before the current frame to N frames after the current frame in a lossless coding form to a first storage area of the storage device.
Step S309, detecting the target behavior of the next frame of video data in the video monitoring data.
It should be noted that, in the steps S301 to S309, N and M are integers greater than or equal to 1, N and M may be set according to the size of the memory space of the storage device, and N and M may be equal or unequal, which is not limited specifically.
After step S307 is executed, a target frame when abnormal behavior occurs in the video data of N frames after the current frame may also be determined; then, according to the current frame and the target frame, the storage identification of the second video data segment is determined and stored in a second storage area of the storage device.
For example, if the time t1 corresponding to the current frame is detected, and abnormal behavior exists in the video data of the N frames after the current frame, the time t2 corresponding to the abnormal behavior occurs, and abnormal behavior does not exist in the video data of the 2N frames after the current frame, t1 and t2 may be stored as storage identifiers of the first video segment to the first storage area of the storage device, for example, the storage identifier of the first video segment is "t 1_ t 2".
After step S308 is executed, the current frame may also be stored as a storage identifier of the first video segment to the first storage area of the storage device.
For example, if the current frame corresponds to time t1, and no abnormal behavior is detected in the video data of N frames after the current frame, t1 may be stored as the storage identifier of the first video segment to the first storage area of the storage device, for example, the storage identifier of the first video segment is "t 1".
Therefore, when the causal consequence of the abnormal behavior segment needs to be traced, the whole video can be traced according to the time line through the named time.
In other possible implementation manners, target behavior detection may also be performed on video data of a preset time length in the video monitoring data, and then, whether abnormal behavior exists in the video data of the preset time length is detected is determined.
In order to more clearly describe the storage method of the video monitoring data provided by the embodiment of the present application, a system structure related to the embodiment of the present application is described below with reference to fig. 4.
In the system, a video decoding module is adopted to decode video monitoring data shot by a camera, the decoded video monitoring data are subjected to abnormal behavior detection by a behavior detection module, if abnormal behaviors are detected, an automatic alarm module carries out automatic alarm prompt, and a lossless coding module stores the video monitoring data corresponding to the abnormal behaviors to a first storage area (namely, an area occupying q% of the video storage module in fig. 4) of storage equipment in a lossless coding mode; if abnormal behavior is detected, the compression encoding module stores the video surveillance data in a compression encoded form in a second storage area of the storage device (i.e., occupying (1-q)% of the area in the video storage module in FIG. 4).
By adopting the storage method, the key part in the monitoring video, namely the video monitoring data corresponding to the abnormal behavior can be clearly stored, the space occupied by each segment is small, the video monitoring data corresponding to the abnormal behavior in a longer time period can be stored, when the abnormal behavior needs to be traced, the efficiency is improved, and the video segments in a longer time period can be provided; and in the same storage space, the video monitoring data without abnormal behaviors are stored at low resolution, so that videos with longer storage periods can be stored.
The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.
Fig. 5 schematically shows a structural diagram of a storage apparatus for video surveillance data according to an embodiment of the present application. As shown in fig. 5, the apparatus has a function of implementing the storage method of the video monitoring data, and the function may be implemented by hardware, or may be implemented by hardware executing corresponding software. The apparatus may include: an acquisition unit 501 and a processing unit 502.
An obtaining unit 501, configured to obtain video monitoring data shot by a camera;
the processing unit 502 is configured to perform target behavior detection on the video monitoring data, store a video monitoring data segment corresponding to an abnormal behavior in a lossless coding form to a first storage area of a storage device when it is detected that the video monitoring data has the abnormal behavior, and store the video monitoring data in a compressed coding form to a second storage area of the storage device;
wherein the first storage area and the second storage area jointly constitute the entire storage area of the storage device.
Optionally, the processing unit 502 is specifically configured to:
performing target behavior detection on current frame video data in the video monitoring data, and performing target behavior detection on video data of N frames behind the current frame when abnormal behavior is detected in the current frame video data; when the abnormal behavior is not detected in the video data of the N frames after the current frame, storing a first video data segment from M frames before the current frame to N frames after the current frame into a first storage area of the storage device in a lossless coding mode;
wherein N and M are integers greater than or equal to 1.
Optionally, the processing unit 502 is further configured to:
and taking the current frame as a storage identifier of the first video segment, and storing the current frame in a first storage area of the storage device.
Optionally, the processing unit 502 is further configured to:
when detecting that abnormal behaviors exist in the video data of the N frames behind the current frame, carrying out target behavior detection on the video data of the 2N frames behind the current frame; when the abnormal behavior is not detected in the video data of the 2N frames after the current frame, storing a second video data segment from M frames before the current frame to the 2N frames after the current frame in a lossless coding mode to a first storage area of the storage device.
Optionally, the processing unit 502 is further configured to:
determining a target frame when abnormal behaviors appear in the video data of the N frames after the current frame; and determining a storage identifier of the second video data segment according to the current frame and the target frame, and storing the storage identifier to a second storage area of the storage device.
Optionally, the processing unit 502 is further configured to:
and when the video monitoring data is not detected to have abnormal behaviors, storing the video monitoring data to a second storage area of the storage device in a compression coding mode.
In the embodiment of the application, by detecting the target behaviors of the video monitoring data, if abnormal behaviors are detected in the video monitoring data, video monitoring data segments corresponding to the abnormal behaviors are stored in a first storage area of a storage device in a lossless coding mode, and the video monitoring data are stored in a second storage area of the storage device in a compressed coding mode; and if the abnormal behavior is not detected in the video monitoring data, storing the video monitoring data to a second storage area of the storage device in a compressed coding mode. By adopting the storage method, the key part in the monitoring video, namely the video monitoring data corresponding to the abnormal behavior can be clearly stored, the space occupied by each segment is small, the video monitoring data corresponding to the abnormal behavior in a longer time period can be stored, when the abnormal behavior needs to be traced, the efficiency is improved, and the video segments in a longer time period can be provided; and in the same storage space, the video monitoring data without abnormal behaviors are stored at low resolution, so that videos with longer storage periods can be stored.
The electronic equipment that this application embodiment provided includes: a memory for storing program instructions; and the processor is used for calling and executing the program instructions in the memory so as to realize the storage method of the video monitoring data in the embodiment.
In this embodiment, the processor and the memory may be connected by a bus or other means. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application specific integrated circuit, or one or more integrated circuits configured to implement embodiments of the present application. The memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, a hard disk, or a solid state disk.
The embodiment of the present application further provides a storage medium, where a computer program is stored in the storage medium, and when at least one processor of a storage device of video monitoring data executes the computer program, the storage device of video monitoring data executes the storage method of video monitoring data according to the foregoing embodiment.
The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a Random Access Memory (RAM).
Those skilled in the art will clearly understand that the techniques in the embodiments of the present application may be implemented by way of software plus a required general hardware platform. Based on such understanding, the technical solutions in the embodiments of the present application may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present application.
The same and similar parts in the various embodiments in this specification may be referred to each other. In particular, for the embodiments of the service construction apparatus and the service loading apparatus, since they are substantially similar to the embodiments of the method, the description is simple, and the relevant points can be referred to the description in the embodiments of the method.
The above-described embodiments of the present application do not limit the scope of the present application.
Claims (10)
1. A method for storing video surveillance data, the method comprising:
acquiring video monitoring data shot by a camera;
performing target behavior detection on the video monitoring data, if abnormal behaviors are detected in the video monitoring data, storing video monitoring data segments corresponding to the abnormal behaviors to a first storage area of a storage device in a lossless coding mode, and storing the video monitoring data to a second storage area of the storage device in a compressed coding mode;
wherein the first storage area and the second storage area jointly constitute the entire storage area of the storage device.
2. The method of claim 1, wherein performing target behavior detection on the video surveillance data, and if abnormal behavior is detected in the video surveillance data, storing video surveillance data segments corresponding to the abnormal behavior in a lossless coding form in a first storage area of a storage device, comprises:
performing target behavior detection on the current frame video data in the video monitoring data, and if abnormal behavior is detected in the current frame video data, performing target behavior detection on the video data of N frames behind the current frame;
if no abnormal behavior is detected in the video data of the N frames after the current frame, storing a first video data segment from M frames before the current frame to N frames after the current frame in a lossless coding mode to a first storage area of the storage device;
wherein N and M are integers greater than or equal to 1.
3. The method of claim 2, further comprising:
and taking the current frame as a storage identifier of the first video segment, and storing the current frame in a first storage area of the storage device.
4. The method of claim 2, further comprising:
if the video data of the N frames behind the current frame is detected to have abnormal behaviors, performing target behavior detection on the video data of the 2N frames behind the current frame;
if no abnormal behavior is detected in the video data of the 2N frames following the current frame, storing a second video data segment from the M frames preceding the current frame to the 2N frames following the current frame in a lossless coding form to a first storage area of the storage device.
5. The method of claim 4, further comprising:
determining a target frame when abnormal behaviors appear in the video data of the N frames after the current frame;
and determining a storage identifier of the second video data segment according to the current frame and the target frame, and storing the storage identifier into a second storage area of the storage device.
6. The method of claim 1, further comprising:
and if the video monitoring data is not detected to have abnormal behaviors, storing the video monitoring data to a second storage area of the storage device in a compressed coding mode.
7. An apparatus for storing video surveillance data, the apparatus comprising:
the acquisition unit is used for acquiring video monitoring data shot by the camera;
the processing unit is used for detecting the target behaviors of the video monitoring data, storing video monitoring data segments corresponding to abnormal behaviors to a first storage area of a storage device in a lossless coding mode when the video monitoring data are detected to have abnormal behaviors, and storing the video monitoring data to a second storage area of the storage device in a compressed coding mode;
wherein the first storage area and the second storage area jointly constitute the entire storage area of the storage device.
8. The apparatus according to claim 7, wherein the processing unit is specifically configured to:
performing target behavior detection on current frame video data in the video monitoring data, and performing target behavior detection on video data of N frames behind the current frame when abnormal behavior is detected in the current frame video data; when the abnormal behavior is not detected in the video data of the N frames after the current frame, storing a first video data segment from M frames before the current frame to N frames after the current frame into a first storage area of the storage device in a lossless coding mode;
wherein N and M are integers greater than or equal to 1.
9. The apparatus of claim 8, wherein the processing unit is further configured to:
when detecting that abnormal behaviors exist in the video data of the N frames behind the current frame, carrying out target behavior detection on the video data of the 2N frames behind the current frame; when the abnormal behavior is not detected in the video data of the 2N frames after the current frame, storing a second video data segment from M frames before the current frame to the 2N frames after the current frame in a lossless coding mode to a first storage area of the storage device.
10. The apparatus of claim 7, wherein the processing unit is further configured to:
and when the video monitoring data is not detected to have abnormal behaviors, storing the video monitoring data to a second storage area of the storage device in a compression coding mode.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011598302.5A CN112291538A (en) | 2020-12-30 | 2020-12-30 | Video monitoring data storage method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011598302.5A CN112291538A (en) | 2020-12-30 | 2020-12-30 | Video monitoring data storage method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112291538A true CN112291538A (en) | 2021-01-29 |
Family
ID=74426685
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011598302.5A Pending CN112291538A (en) | 2020-12-30 | 2020-12-30 | Video monitoring data storage method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112291538A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116743948A (en) * | 2023-06-15 | 2023-09-12 | 上海山源电子科技股份有限公司 | Method, device and equipment for monitoring video recording and computer readable storage medium |
| CN117558451A (en) * | 2024-01-11 | 2024-02-13 | 广州中大医疗器械有限公司 | Nerve loss degree assessment method based on big data |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090015671A1 (en) * | 2007-07-13 | 2009-01-15 | Honeywell International, Inc. | Features in video analytics |
| CN101742294A (en) * | 2008-11-14 | 2010-06-16 | 北京中星微电子有限公司 | Method and device for enhancing monitoring video compression ratio |
| CN101778265A (en) * | 2010-01-13 | 2010-07-14 | 上海交通大学 | Exception event detection and double-stream coding-based video monitoring method |
| CN101917602A (en) * | 2010-08-26 | 2010-12-15 | 四川大学 | Digital video intelligent monitoring equipment based on high-resolution camera and data processing method |
| CN105978158A (en) * | 2016-06-28 | 2016-09-28 | 无锡易胜捷科技有限公司 | Intelligent video monitoring system for transformer station |
| CN109597572A (en) * | 2018-11-22 | 2019-04-09 | 上海新储集成电路有限公司 | A kind of memory management method |
-
2020
- 2020-12-30 CN CN202011598302.5A patent/CN112291538A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090015671A1 (en) * | 2007-07-13 | 2009-01-15 | Honeywell International, Inc. | Features in video analytics |
| CN101742294A (en) * | 2008-11-14 | 2010-06-16 | 北京中星微电子有限公司 | Method and device for enhancing monitoring video compression ratio |
| CN101778265A (en) * | 2010-01-13 | 2010-07-14 | 上海交通大学 | Exception event detection and double-stream coding-based video monitoring method |
| CN101917602A (en) * | 2010-08-26 | 2010-12-15 | 四川大学 | Digital video intelligent monitoring equipment based on high-resolution camera and data processing method |
| CN105978158A (en) * | 2016-06-28 | 2016-09-28 | 无锡易胜捷科技有限公司 | Intelligent video monitoring system for transformer station |
| CN109597572A (en) * | 2018-11-22 | 2019-04-09 | 上海新储集成电路有限公司 | A kind of memory management method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116743948A (en) * | 2023-06-15 | 2023-09-12 | 上海山源电子科技股份有限公司 | Method, device and equipment for monitoring video recording and computer readable storage medium |
| CN117558451A (en) * | 2024-01-11 | 2024-02-13 | 广州中大医疗器械有限公司 | Nerve loss degree assessment method based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR101216271B1 (en) | camera tampering detection | |
| CN112511821B (en) | Video jamming detection method and device and storage medium | |
| CN111629165B (en) | Alarm video processing method, device, equipment and storage medium | |
| CN112291538A (en) | Video monitoring data storage method and device | |
| US20120078864A1 (en) | Electronic data integrity protection device and method and data monitoring system | |
| KR101949676B1 (en) | syntax-based method of providing intrusion detection in compressed video | |
| WO2022213336A1 (en) | Vehicle driving environment abnormality monitoring method and apparatus, electronic device, and storage medium | |
| CN109815839B (en) | Loitering person identification method under micro-service architecture and related product | |
| CN110647818A (en) | Identification method and device for shielding target object | |
| Aloraini et al. | Statistical sequential analysis for object-based video forgery detection | |
| US11836935B2 (en) | Method and apparatus for detecting motion deviation in a video | |
| CN108629310B (en) | Engineering management supervision method and device | |
| CN113114986B (en) | Early warning method based on picture and sound synchronization and related equipment | |
| CN113486856A (en) | Driver irregular behavior detection method based on semantic segmentation and convolutional neural network | |
| Ren et al. | Frame duplication forgery detection and localization algorithm based on the improved Levenshtein distance | |
| CN111369591A (en) | Method, device and equipment for tracking moving object | |
| US10194072B2 (en) | Method and apparatus for remote detection of focus hunt | |
| CN110798656A (en) | Method, device, medium and equipment for processing monitoring video file | |
| US20190188861A1 (en) | Method and apparatus for detecting motion deviation in a video sequence | |
| CN116189109A (en) | Model training method, road event detection method, device and related equipment | |
| CN113726779B (en) | Rule false alarm testing method and device, electronic equipment and computer storage medium | |
| CN115147756A (en) | Video stream processing method and device, electronic equipment and storage medium | |
| US11398091B1 (en) | Repairing missing frames in recorded video with machine learning | |
| US20110234912A1 (en) | Image activity detection method and apparatus | |
| CN113938673A (en) | Smart city monitoring management system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210129 |
|
| RJ01 | Rejection of invention patent application after publication |