CN116208785A - Video analysis method, device and storage medium based on cloud service - Google Patents

Video analysis method, device and storage medium based on cloud service Download PDF

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Publication number
CN116208785A
CN116208785A CN202310217229.XA CN202310217229A CN116208785A CN 116208785 A CN116208785 A CN 116208785A CN 202310217229 A CN202310217229 A CN 202310217229A CN 116208785 A CN116208785 A CN 116208785A
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video
data
encrypted
processing node
cloud
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张传金
刘治国
万海峰
陶维俊
姚莉莉
邵磊
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ANHUI CREARO TECHNOLOGY CO LTD
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ANHUI CREARO TECHNOLOGY CO LTD
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/234—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs
    • H04N21/23418—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/5038—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/234—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs
    • H04N21/2347—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving video stream encryption
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
    • H04N21/83—Generation or processing of protective or descriptive data associated with content; Content structuring
    • H04N21/845—Structuring of content, e.g. decomposing content into time segments
    • H04N21/8456—Structuring of content, e.g. decomposing content into time segments by decomposing the content in the time domain, e.g. in time segments
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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  • Theoretical Computer Science (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The invention provides a video analysis method, a device and a storage medium based on cloud services. The method comprises the steps of obtaining video data information and analysis requirement information of the video data, and executing segmentation processing on the video data information according to the analysis requirement information to generate a plurality of video clips; acquiring a first performance characteristic sequence corresponding to each video segment and a second characteristic sequence corresponding to a link between a local cloud processing node and each cloud processing node; comparing the second characteristic sequence and the first performance characteristic sequence corresponding to each cloud processing node, determining the optimal cloud processing node corresponding to each video segment, and generating a first processing mapping relation; and respectively sending each video clip to a corresponding cloud processing node according to the first processing mapping relation for video analysis. Compared with the prior art, the optimal processing node distribution is realized by determining the optimal processing node through comparing the characteristics of different video segments and cloud processing nodes.

Description

Video analysis method, device and storage medium based on cloud service
Technical Field
The invention relates to the field of video processing, in particular to a video analysis method, device and storage medium based on cloud service.
Background
With the development of economy, the data volume of the monitoring video shows explosive growth. The video monitoring devices monitor and record the real-time operation conditions of key places, and the stable operation of the society is maintained. The video monitoring system application comprises the following layers, wherein the first layer is on-site video monitoring, and consists of a front-end camera, a video recorder, a video display and the like, and supports on-site monitoring, monitoring video storage and the like; the second level is remote video monitoring, which consists of a monitoring front end, a control console and a transmission network, and supports remote monitoring systems such as a transformer substation, a ground city monitoring center and the like which are suitable for unmanned duty. The third layer is integrated into the emergency command system, so that each level of monitoring video is interconnected with the corresponding emergency command center, and the emergency command center is supplied with images of the field video.
In the prior art, the data processing of video information is often the traditional batch server processing, and it is difficult to perform corresponding allocation to different node processing according to intelligent allocation or segmentation. Therefore, it is highly desirable to propose a distribution and cooperative control processing scheme for each task calculation amount and video content execution.
Disclosure of Invention
In view of the above, an object of an embodiment of the present invention is to provide a processing node allocation scheme that is optimal by determining optimal processing nodes through comparison of characteristics of different video segments and cloud processing nodes.
The first aspect of the invention provides a video analysis method based on cloud service, which comprises the following steps:
acquiring video data information and analysis demand information of the video data, and executing segmentation processing on the video data information according to the analysis demand information to generate a plurality of video clips;
acquiring a first performance characteristic sequence corresponding to each video segment and a second characteristic sequence corresponding to a link between a local cloud processing node and each cloud processing node;
comparing the second characteristic sequence and the first performance characteristic sequence corresponding to each cloud processing node, determining the optimal cloud processing node corresponding to each video segment, and generating a first processing mapping relation;
and respectively sending each video clip to a corresponding cloud processing node according to the first processing mapping relation for video analysis.
Further, the performing a segmentation process on the video data information according to the analysis requirement information to generate a plurality of video clips includes: performing segmentation processing on the video data information according to the importance degree of the video content; the importance degree at least comprises a role importance degree;
different importance levels are set for different video clips, and first performance characteristic sequence parameters corresponding to the importance levels are determined; the first performance characteristic sequence parameters include bandwidth, delay and priority.
Further, the obtaining a first performance feature sequence corresponding to each video clip and a second feature sequence corresponding to a link between the local cloud processing node and each cloud processing node includes:
generating a first performance characteristic sequence according to the first performance characteristic sequence parameter;
reading bandwidth, delay and priority information corresponding to links between the local cloud processing nodes to generate a second feature sequence;
further, the comparing the second feature sequence and the first performance feature sequence corresponding to each cloud processing node, determining an optimal cloud processing node corresponding to each video segment, and generating a first processing mapping relationship includes:
and calculating Euclidean distance between the first performance feature sequence and the second feature sequence, taking a cloud processing node corresponding to the minimum Euclidean distance as an optimal cloud processing node corresponding to the video segment, and generating a first processing mapping relation.
Further, the video analysis requirements include video concentration;
each video clip is respectively sent to a corresponding cloud processing node for video analysis according to the first processing mapping relation, and the method comprises the following steps:
acquiring a face area of a character and determining the face area as an area to be encrypted;
encrypting the source data of each area to be encrypted to obtain encrypted data;
if the encrypted data is larger than the source data of the area to be encrypted, replacing the source data corresponding to the area to be encrypted with first data in the encrypted data, and recording second data in the encrypted data to obtain a target video comprising records; the size of the first data is equal to the size of the data corresponding to the area to be encrypted, and the second data is the rest data except the first data in the encrypted data.
In a second aspect, the present embodiment further proposes a video analysis device for cloud services, where the device includes:
the first acquisition module is used for acquiring video data information and analysis demand information of the video data, and executing segmentation processing on the video data information according to the analysis demand information to generate a plurality of video clips;
the second acquisition module is used for acquiring a first performance characteristic sequence corresponding to each video clip and a second characteristic sequence corresponding to a link between the local cloud processing node and each cloud processing node;
the generation module is used for comparing the second characteristic sequence and the first performance characteristic sequence corresponding to each cloud processing node, determining the optimal cloud processing node corresponding to each video segment and generating a first processing mapping relation;
and the processing module is used for respectively sending each video clip to a corresponding cloud processing node according to the first processing mapping relation to carry out video analysis.
Further, the first acquisition module is further configured to perform segmentation processing on the video data information according to the importance level of the video content; the importance degree at least comprises a role importance degree;
different importance levels are set for different video clips, and first performance characteristic sequence parameters corresponding to the importance levels are determined; the first performance characteristic sequence parameters include bandwidth, delay and priority.
Further, the second obtaining module is further configured to generate a first performance feature sequence according to the first performance feature sequence parameter; and reading the bandwidth, delay and priority information corresponding to the links between the local cloud processing nodes to generate a second characteristic sequence.
Further, the video analysis requirements include video concentration;
the processing module is also used for acquiring the face area of the character and determining the face area as an area to be encrypted; encrypting the source data of each area to be encrypted to obtain encrypted data; if the encrypted data is larger than the source data of the area to be encrypted, replacing the source data corresponding to the area to be encrypted with first data in the encrypted data, and recording second data in the encrypted data to obtain a target video comprising records; the size of the first data is equal to the size of the data corresponding to the area to be encrypted, and the second data is the rest data except the first data in the encrypted data.
Further, a third aspect of the present invention also provides a storage medium storing a computer program; the program is loaded and executed by a processor to implement the video analysis method steps of the cloud service as described above.
In the scheme of the invention, the video data information and the analysis demand information of the video data are obtained, and the segmentation processing is carried out on the video data information according to the analysis demand information to generate a plurality of video clips; acquiring a first performance characteristic sequence corresponding to each video segment and a second characteristic sequence corresponding to a link between a local cloud processing node and each cloud processing node; comparing the second characteristic sequence and the first performance characteristic sequence corresponding to each cloud processing node, determining the optimal cloud processing node corresponding to each video segment, and generating a first processing mapping relation; and respectively sending each video clip to a corresponding cloud processing node according to the first processing mapping relation for video analysis. Compared with the prior art, the optimal processing node distribution is realized by determining the optimal processing node through comparing the characteristics of different video segments and cloud processing nodes.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow diagram of a video analysis method based on cloud services according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a video analysis device based on cloud services according to an embodiment of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present application. One skilled in the relevant art will recognize, however, that the aspects of the application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
It should be noted that: references herein to "a plurality" means two or more.
The implementation details of the technical solutions of the embodiments of the present application are described in detail below:
referring to fig. 1, fig. 1 is a schematic flow chart of a video analysis method based on cloud service according to an embodiment of the present invention. As shown in fig. 1, a video analysis method based on cloud service according to an embodiment of the present invention includes:
s1, acquiring video data information and analysis requirement information of the video data, and executing segmentation processing on the video data information according to the analysis requirement information to generate a plurality of video clips.
Further, the performing a segmentation process on the video data information according to the analysis requirement information to generate a plurality of video clips includes: performing segmentation processing on the video data information according to the importance degree of the video content; the importance degree at least comprises a role importance degree; the video analysis requirements include video concentration.
In this embodiment, local video segmentation may be actually performed for a certain or a few interesting roles to form a video segment with a role tag, and subsequent calculation and determination of cloud nodes may be performed according to the requirement of video concentration.
Different importance levels are set for different video clips, and first performance characteristic sequence parameters corresponding to the importance levels are determined; the first performance characteristic sequence parameters comprise a bandwidth a1, a delay b1 and a priority c1; the first performance characteristic sequence is { a1, b1, c1}.
In this embodiment, for example, the highest priority is set for the main angle 1, and a high level of bandwidth, low latency and high priority are required for the video clip where the main angle 1 is located, so that the corresponding performance parameters can be set.
S2, acquiring a first performance characteristic sequence corresponding to each video segment and a second characteristic sequence corresponding to a link between the local cloud processing nodes.
Further, in this embodiment, the obtaining a first performance feature sequence corresponding to each video clip and a second feature sequence corresponding to a link between a local cloud processing node and each cloud processing node includes:
generating a first performance characteristic sequence according to the first performance characteristic sequence parameter; and reading the bandwidth a2, the delay b2 and the priority information c2 corresponding to the links between the local cloud processing nodes to generate a second feature sequence { a2, b2, c2}.
S3, comparing the second characteristic sequence and the first performance characteristic sequence corresponding to each cloud processing node, determining the optimal cloud processing node corresponding to each video segment, and generating a first processing mapping relation;
further, in this embodiment, comparing the second feature sequence and the first performance feature sequence corresponding to each cloud processing node, determining an optimal cloud processing node corresponding to each video clip, and generating a first processing mapping relationship includes:
and calculating Euclidean distance between the first performance feature sequence and the second feature sequence, taking a cloud processing node corresponding to the minimum Euclidean distance as an optimal cloud processing node corresponding to the video segment, and generating a first processing mapping relation.
In this embodiment, the euclidean distance between { a1, b1, c1}, { a2, b2, c2} is calculated, the cloud processing node corresponding to the minimum euclidean distance is taken as the best cloud processing node corresponding to the video clip, and a second mapping relationship between the video clip identifier and the cloud processing node is generated.
And S4, respectively sending each video clip to a corresponding cloud processing node according to the first processing mapping relation to perform video analysis.
In the embodiment of the invention, considering that the possibility of data before and after encryption is inconsistent, for example, in the case of adopting asymmetric encryption, because of message redundancy, the encrypted data is generally larger than the data before encryption, if the encrypted data is larger, the encrypted data cannot be written back into the original video area (because the data amount which can be contained in one area is definite and cannot be large or cannot be small), therefore, the first data with the same size as the data corresponding to the original area in the encrypted data can be written into the area to be encrypted in the source video, the data corresponding to the area to be encrypted in the source video is replaced, and the second data except the first data is written into the record in a certain format.
Each video clip is respectively sent to a corresponding cloud processing node for video analysis according to the first processing mapping relation, and the method comprises the following steps:
acquiring a face area of a character and determining the face area as an area to be encrypted; encrypting the source data of each area to be encrypted to obtain encrypted data;
if the encrypted data is larger than the source data of the area to be encrypted, replacing the source data corresponding to the area to be encrypted with first data in the encrypted data, and recording second data in the encrypted data to obtain a target video comprising records; the size of the first data is equal to the size of the data corresponding to the area to be encrypted, and the second data is the rest data except the first data in the encrypted data.
In addition, as shown in fig. 2, a second aspect of the embodiment of the present application further discloses a video analysis device of a cloud service, where the device includes:
a first obtaining module 10, configured to obtain video data information and analysis requirement information of the video data, and perform segmentation processing on the video data information according to the analysis requirement information to generate a plurality of video clips;
the second obtaining module 20 is configured to obtain a first performance feature sequence corresponding to each video segment, and a second feature sequence corresponding to a link between the local cloud processing node and each cloud processing node;
the generating module 30 is configured to compare the second feature sequence and the first performance feature sequence corresponding to each cloud processing node, determine an optimal cloud processing node corresponding to each video segment, and generate a first processing mapping relationship;
and the processing module 40 is configured to send each video clip to a corresponding cloud processing node for video analysis according to the first processing mapping relationship.
The first obtaining module 10 is further configured to perform segmentation processing on the video data information according to the importance level of the video content; the importance degree at least comprises a role importance degree;
different importance levels are set for different video clips, and first performance characteristic sequence parameters corresponding to the importance levels are determined; the first performance characteristic sequence parameters include bandwidth, delay and priority.
The second obtaining module 20 is further configured to generate a first performance characteristic sequence according to the first performance characteristic sequence parameter; and reading the bandwidth, delay and priority information corresponding to the links between the local cloud processing nodes to generate a second characteristic sequence.
The generating module 30 is further configured to calculate the euclidean distance between the first performance feature sequence and the second feature sequence, take the cloud processing node corresponding to the minimum euclidean distance as the optimal cloud processing node corresponding to the video segment, and generate the first processing mapping relationship.
The video analysis requirements include video concentration;
the processing module 40 is further configured to acquire a face area of the character and determine the face area as an area to be encrypted; encrypting the source data of each area to be encrypted to obtain encrypted data; if the encrypted data is larger than the source data of the area to be encrypted, replacing the source data corresponding to the area to be encrypted with first data in the encrypted data, and recording second data in the encrypted data to obtain a target video comprising records; the size of the first data is equal to the size of the data corresponding to the area to be encrypted, and the second data is the rest data except the first data in the encrypted data.
In addition, the embodiment of the application also discloses an electronic device, which comprises: one or more processors, memory for storing one or more computer programs; characterized in that the computer program is configured to be executed by the one or more processors, the program comprising method steps for performing a cloud service based video analysis as described above.
Furthermore, the embodiment of the application also provides a storage medium, and the storage medium stores a computer program; the program is loaded and executed by a processor to implement the cloud service based video analysis method steps as described above.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.
The elements described as separate components may or may not be physically separate, and as such, those skilled in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, where the elements and steps of the examples are generally described functionally in the foregoing description of the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention is essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a grid device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (10)

1. A video analysis method based on cloud services, the method comprising:
acquiring video data information and analysis demand information of the video data, and executing segmentation processing on the video data information according to the analysis demand information to generate a plurality of video clips;
acquiring a first performance characteristic sequence corresponding to each video segment and a second characteristic sequence corresponding to a link between a local cloud processing node and each cloud processing node;
comparing the second characteristic sequence and the first performance characteristic sequence corresponding to each cloud processing node, determining the optimal cloud processing node corresponding to each video segment, and generating a first processing mapping relation;
and respectively sending each video clip to a corresponding cloud processing node according to the first processing mapping relation for video analysis.
2. The cloud service-based video analysis method according to claim 1, wherein the performing a segmentation process on video data information according to the analysis demand information generates a plurality of video clips, comprising: performing segmentation processing on the video data information according to the importance degree of the video content; the importance degree at least comprises a role importance degree;
different importance levels are set for different video clips, and first performance characteristic sequence parameters corresponding to the importance levels are determined; the first performance characteristic sequence parameters include bandwidth, delay and priority.
3. The cloud service-based video analysis method according to claim 2, wherein the obtaining a first performance feature sequence corresponding to each video clip and a second feature sequence corresponding to a link between a local and each cloud processing node includes:
generating a first performance characteristic sequence according to the first performance characteristic sequence parameter;
and reading the bandwidth, delay and priority information corresponding to the links between the local cloud processing nodes to generate a second characteristic sequence.
4. The cloud service-based video analysis method according to claim 3, wherein the comparing the second feature sequence and the first performance feature sequence corresponding to each cloud processing node, determining an optimal cloud processing node corresponding to each video clip, and generating the first processing mapping relationship includes:
and calculating Euclidean distance between the first performance feature sequence and the second feature sequence, taking a cloud processing node corresponding to the minimum Euclidean distance as an optimal cloud processing node corresponding to the video segment, and generating a first processing mapping relation.
5. The cloud service based video analytics method of claim 4, wherein the video analytics demand includes video concentration;
each video clip is respectively sent to a corresponding cloud processing node for video analysis according to the first processing mapping relation, and the method comprises the following steps:
acquiring a face area of a character and determining the face area as an area to be encrypted;
encrypting the source data of each area to be encrypted to obtain encrypted data;
if the encrypted data is larger than the source data of the area to be encrypted, replacing the source data corresponding to the area to be encrypted with first data in the encrypted data, and recording second data in the encrypted data to obtain a target video comprising records; the size of the first data is equal to the size of the data corresponding to the area to be encrypted, and the second data is the rest data except the first data in the encrypted data.
6. A video analysis device for cloud services, the device comprising:
the first acquisition module is used for acquiring video data information and analysis demand information of the video data, and executing segmentation processing on the video data information according to the analysis demand information to generate a plurality of video clips;
the second acquisition module is used for acquiring a first performance characteristic sequence corresponding to each video clip and a second characteristic sequence corresponding to a link between the local cloud processing node and each cloud processing node;
the generation module is used for comparing the second characteristic sequence and the first performance characteristic sequence corresponding to each cloud processing node, determining the optimal cloud processing node corresponding to each video segment and generating a first processing mapping relation;
and the processing module is used for respectively sending each video clip to a corresponding cloud processing node according to the first processing mapping relation to carry out video analysis.
7. The video analysis device of cloud services of claim 6, wherein said first acquisition module is further configured to perform segmentation processing on said video data information according to a degree of importance of video content; the importance degree at least comprises a role importance degree;
different importance levels are set for different video clips, and first performance characteristic sequence parameters corresponding to the importance levels are determined; the first performance characteristic sequence parameters include bandwidth, delay and priority.
8. The video analysis device of claim 7, wherein the second acquisition module is further configured to generate a first performance feature sequence according to the first performance feature sequence parameter; and reading the bandwidth, delay and priority information corresponding to the links between the local cloud processing nodes to generate a second characteristic sequence.
9. The cloud service-based video analytics method of claim 8, wherein the video analytics demand includes video concentration;
the processing module is also used for acquiring the face area of the character and determining the face area as an area to be encrypted; encrypting the source data of each area to be encrypted to obtain encrypted data; if the encrypted data is larger than the source data of the area to be encrypted, replacing the source data corresponding to the area to be encrypted with first data in the encrypted data, and recording second data in the encrypted data to obtain a target video comprising records; the size of the first data is equal to the size of the data corresponding to the area to be encrypted, and the second data is the rest data except the first data in the encrypted data.
10. A storage medium storing a computer program; characterized in that the program is loaded and executed by a processor to implement the video analysis method steps of the cloud service according to claims 1-5.
CN202310217229.XA 2023-03-03 2023-03-03 Video analysis method, device and storage medium based on cloud service Pending CN116208785A (en)

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