CN112200067A - Intelligent video event detection method, system, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides an intelligent video event detection method, an intelligent video event detection system, electronic equipment and a storage medium, wherein the method comprises the steps of obtaining a video source from a database, labeling the video source, and determining a corresponding preset video template according to the label of the video source; determining key frames of a video source, splitting the video source into a plurality of video segments with the same number of frames according to the key frames, scoring and sequencing the plurality of video segments according to a preset video template based on a preset detection model, and determining the video segment with the highest score as a target video segment; and when the highest score of the scores is greater than or equal to a preset threshold value, determining frames with the same timestamp in the target video clip and corresponding to the marked frames in the preset video template as an event evidence graph, and simultaneously determining the corresponding event type. The method can intelligently and automatically detect the event in the video and output the detection result, and meanwhile, the detection result has the key image evidence of the event, so that the event detection efficiency is improved, and the event detection is more accurate and reliable.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to an intelligent video event detection method, an intelligent video event detection system, electronic equipment and a storage medium.
Background
With the improvement of social and economic levels, the technology of intelligently detecting the content of videos and then specifically applying the content to specific scenes to realize the fact that the judgment of events of the specific scenes is popular, such as video detection of traffic accidents, video monitoring of anti-theft and public security, statistics of traffic flows and the like. The intelligent video event detection technology can be used for greatly improving the detection efficiency.
At present, the identification and detection of video events mainly depend on manual screening and browsing, or only part of the video events can be identified, and a manual screening part is also needed, so that the video content cannot be intelligently and quickly identified to further judge the event information of a specific scene, and the detection efficiency is low.
Disclosure of Invention
A first objective of the embodiments of the present invention is to provide an intelligent video event detection method, which is to solve the problem that the current video event detection efficiency is low.
The embodiment of the invention is realized in such a way that an intelligent video event detection method comprises the following steps:
acquiring a video source from a database, labeling the video source, and determining a corresponding preset video template according to the label of the video source;
determining key frames of the video source, splitting the video source into a plurality of video clips with the same number of frames according to the key frames, performing scoring and sequencing on the plurality of video clips according to a preset video template based on a preset detection model, and determining the video clip with the highest score as a target video clip;
and when the highest score of the scores is greater than or equal to a preset threshold value, determining frames with the same timestamp in the target video clip and corresponding to the marked frames in the preset video template as an event evidence graph, and simultaneously determining the corresponding event type.
In one embodiment, the acquiring a video source from a database, tagging the video source, and determining a corresponding preset video template according to the tag of the video source includes: acquiring a video to be detected from a system database, labeling the video source according to basic information of the video to be detected, and selecting a corresponding preset video template from a preset video template library according to the label of the video source; the video to be detected is the video with the earliest shooting time in all the videos to be detected in the data, and the basic information comprises video shooting position information, video shooting time information and corresponding equipment information of the video shooting device.
In one embodiment, the preset video template includes a plurality of videos having tags and having the same frame as the target video segment, and the frames of the preset video template are subjected to gray processing, and the tags of the preset video template include position information.
In one embodiment, the selecting a corresponding preset video template from a preset video template library according to the label of the video source includes; traversing the preset video template library according to the position information of the video source, and determining a preset video template with the position information identical to that of the video source as a corresponding preset video template.
In one embodiment, the scoring and sorting the video segments according to the preset video template based on a preset detection model includes: graying each frame of the video clip, calculating the similarity of each frame by similarity calculation of each frame of the video clip and a frame of a preset video template at the same time stamp based on an image similarity calculation model, calculating the similarity of each frame by weighting all the frames of the video clip to obtain the similarity of the video clip and the preset video template, and scoring and sequencing the video clips according to the similarity, wherein the higher the similarity is, the higher the score is, and the higher the score is, the earlier the sequencing is.
Another objective of an embodiment of the present invention is to provide an intelligent video event detection system, including:
the video acquisition unit is used for acquiring a video source from a database, labeling the video source and determining a corresponding preset video template according to the label of the video source;
the target video determining unit is used for determining key frames of the video source, splitting the video source into a plurality of video clips with the same number of frames according to the key frames, performing scoring and sequencing on the plurality of video clips according to the preset video template based on a preset detection model, and determining the video clip with the highest score as a target video clip;
and the detection result determining unit is used for determining frames with the same timestamp in the target video clip and corresponding to the marked frames in the preset video template as event evidence graphs and determining corresponding event types when the highest score of the scores is greater than or equal to a preset threshold value.
In one embodiment, the acquiring a video source from a database, tagging the video source, and determining a corresponding preset video template according to the tag of the video source includes: acquiring a video to be detected from a system database, labeling the video source according to basic information of the video to be detected, and selecting a corresponding preset video template from a preset video template library according to the label of the video source; the video to be detected is the video with the earliest shooting time in all the videos to be detected in the data, and the basic information comprises video shooting position information, video shooting time information and corresponding equipment information of the video shooting device.
In one embodiment, the preset video template includes a plurality of videos having tags and having the same frame as the target video segment, and the frames of the preset video template are subjected to gray processing, and the tags of the preset video template include position information.
It is a further object of an embodiment of the present invention to provide an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to execute the steps of the intelligent video event detection method.
It is yet another object of an embodiment of the present invention to provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, causes the processor to perform the steps of the intelligent video event detection method.
According to the intelligent video event detection method provided by the embodiment of the invention, a video source is obtained from a database, the video source is labeled, and a corresponding preset video template is determined according to the label of the video source; determining key frames of the video source, splitting the video source into a plurality of video clips with the same number of frames according to the key frames, performing scoring and sequencing on the plurality of video clips according to a preset video template based on a preset detection model, and determining the video clip with the highest score as a target video clip; and when the highest score of the scores is greater than or equal to a preset threshold value, determining frames with the same timestamp in the target video clip and corresponding to the marked frames in the preset video template as an event evidence graph, and simultaneously determining the corresponding event type. The event detection method has the advantages that the event in the video can be intelligently and automatically detected and the detection result is output, meanwhile, the key image evidence of the event exists in the detection result, on one hand, the event detection efficiency is improved, and on the other hand, the event detection is more accurate and reliable.
Drawings
Fig. 1 is a flow chart of an implementation of an intelligent video event detection method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of main modules of an intelligent video event detection system according to an embodiment of the present invention;
FIG. 3 provides an exemplary system architecture diagram that may be employed in accordance with an embodiment of the present invention;
fig. 4 is a schematic block diagram of a computer system suitable for implementing a terminal device or a server according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, etc. may be used to describe various information in embodiments of the present invention, the information should not be limited by these terms. These terms are only used to distinguish one type of information from another.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
To further explain the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the embodiments, structures, features and effects according to the present invention will be given with reference to the accompanying drawings and preferred embodiments.
Fig. 1 shows an implementation flow of an intelligent video event detection method provided by an embodiment of the present invention, and for convenience of description, only parts related to the embodiment of the present invention are shown, which are detailed as follows:
an intelligent video event detection method, comprising:
s101: acquiring a video source from a database, labeling the video source, and determining a corresponding preset video template according to the label of the video source;
s102: determining key frames of the video source, splitting the video source into a plurality of video clips with the same number of frames according to the key frames, performing scoring and sequencing on the plurality of video clips according to a preset video template based on a preset detection model, and determining the video clip with the highest score as a target video clip;
s103: and when the highest score of the scores is greater than or equal to a preset threshold value, determining frames with the same timestamp in the target video clip and corresponding to the marked frames in the preset video template as an event evidence graph, and simultaneously determining the corresponding event type.
In step S101, a video source is acquired from a database, the video source is labeled, and a corresponding preset video template is determined according to the label of the video source, so that a video shot by a video shooting device can be acquired for detection, and the acquired video source is labeled, so that a preset video template corresponding to the label can be selected from a preset video template library according to the label.
In one embodiment, the acquiring a video source from a database, tagging the video source, and determining a corresponding preset video template according to the tag of the video source includes: acquiring a video to be detected from a system database, labeling the video source according to basic information of the video to be detected, and selecting a corresponding preset video template from a preset video template library according to the label of the video source; the video to be detected is the video with the earliest shooting time in all the videos to be detected in the data, and the basic information comprises video shooting position information, video shooting time information and corresponding equipment information of the video shooting device. Therefore, basic information of the video source, such as the source (equipment information, which equipment shoots), geographical position information, shooting time information and the like of the video source, can be determined through the label, a preset video template matched with the label can be selected in a preset video template library through the label, a specific geographical position corresponding to the event type can also be determined through the label, and when the event is required to be traced, the tracing can be performed according to the source of the video source reflected by the label.
In one embodiment, the preset video template includes a plurality of videos having tags and having the same frame as the target video segment, and the frames of the preset video template are subjected to gray processing, and the tags of the preset video template include position information.
In one embodiment, the selecting a corresponding preset video template from a preset video template library according to the label of the video source includes; traversing the preset video template library according to the position information of the video source, and determining a preset video template with the position information identical to that of the video source as a corresponding preset video template.
Specifically, for example, a video source is obtained, the video source is shot by a device a at a street X intersection B, the shooting time is 9 am 3/2/2020, the video source can be labeled as "street X intersection B, 9 am 3/2/a", and a video template labeled as street X intersection B is matched in a preset video template library to serve as a corresponding preset template.
Therefore, a video source is obtained from a database, the video source is labeled, and a corresponding preset video template is determined according to the label of the video source; determining key frames of the video source, splitting the video source into a plurality of video clips with the same number of frames according to the key frames, performing scoring and sequencing on the plurality of video clips according to a preset video template based on a preset detection model, and determining the video clip with the highest score as a target video clip; and when the highest score of the scores is greater than or equal to a preset threshold value, determining frames with the same timestamp in the target video clip and corresponding to the marked frames in the preset video template as an event evidence graph, and simultaneously determining the corresponding event type. The intelligent video event detection method can intelligently and automatically detect the events in the video and output the detection results, and meanwhile, the detection results have key image evidences of the events, so that on one hand, the event detection efficiency is improved, and on the other hand, the event detection is more accurate and reliable.
In step S102: determining key frames of the video source, splitting the video source into a plurality of video segments with the same number of frames according to the key frames, scoring and sequencing the plurality of video segments according to a preset video template based on a preset detection model, and determining the video segment with the highest score as a target video segment, so that whether the video source is a video meeting an event type can be determined, and no subsequent processing is required.
In step S103: and when the highest score of the scores is greater than or equal to a preset threshold value, determining frames with the same timestamp in the target video clip and corresponding to the marked frames in the preset video template as an event evidence graph, and simultaneously determining the corresponding event type.
Here, the preset threshold may be set according to a specific scenario, for example, in terms of vehicle violation information monitoring, the preset threshold may be set to 95%, and in the event of accident detection, the preset threshold may be set to 80%.
In one embodiment, the determined time evidence graph and the corresponding event type generation interface can be output to the client for display, and the interface information can include the evidence graph, the event time and the event type, for example, when the video source is a video source for a vehicle to run at a crossing B of a street X at 3/2/9 am in 2020, the interface information includes the evidence graph of vehicle line pressing, the line pressing time and the line pressing violation.
In one embodiment, the scoring and sorting the video segments according to the preset video template based on a preset detection model includes: graying each frame of the video clip, calculating the similarity of each frame by similarity calculation of each frame of the video clip and a frame of a preset video template at the same time stamp based on an image similarity calculation model, calculating the similarity of each frame by weighting all the frames of the video clip to obtain the similarity of the video clip and the preset video template, and scoring and sequencing the video clips according to the similarity, wherein the higher the similarity is, the higher the score is, and the higher the score is, the earlier the sequencing is.
Fig. 2 is a schematic diagram illustrating main modules of an intelligent video event detection system according to an embodiment of the present invention, and for convenience of description, only the parts related to the embodiment of the present invention are shown, which are detailed as follows:
an intelligent video event detection system 200, comprising:
the video acquisition unit 201 is configured to acquire a video source from a database, tag the video source, and determine a corresponding preset video template according to the tag of the video source;
a target video determining unit 202, configured to determine a key frame of the video source, split the video source into a plurality of video segments with the same number of frames according to the key frame, score and sort the plurality of video segments according to the preset video template based on a preset detection model, and determine a video segment with a highest score as a target video segment;
the detection result determining unit 203 is configured to determine, when the highest score of the scores is greater than or equal to a preset threshold, a frame with the same timestamp in the target video segment and corresponding to a marker frame in the preset video template as an event evidence map, and determine a corresponding event type at the same time.
In one embodiment, the acquiring a video source from a database, tagging the video source, and determining a corresponding preset video template according to the tag of the video source includes: acquiring a video to be detected from a system database, labeling the video source according to basic information of the video to be detected, and selecting a corresponding preset video template from a preset video template library according to the label of the video source; the video to be detected is the video with the earliest shooting time in all the videos to be detected in the data, and the basic information comprises video shooting position information, video shooting time information and corresponding equipment information of the video shooting device. Therefore, basic information of the video source, such as the source (equipment information, which equipment shoots), geographical position information, shooting time information and the like of the video source, can be determined through the label, a preset video template matched with the label can be selected in a preset video template library through the label, a specific geographical position corresponding to the event type can also be determined through the label, and when the event is required to be traced, the tracing can be performed according to the source of the video source reflected by the label.
In one embodiment, the preset video template includes a plurality of videos having tags and having the same frame as the target video segment, and the frames of the preset video template are subjected to gray processing, and the tags of the preset video template include position information.
In one embodiment, the selecting a corresponding preset video template from a preset video template library according to the label of the video source includes; traversing the preset video template library according to the position information of the video source, and determining a preset video template with the position information identical to that of the video source as a corresponding preset video template.
Specifically, for example, a video source is obtained, the video source is shot by a device a at a street X intersection B, the shooting time is 9 am 3/2/2020, the video source can be labeled as "street X intersection B, 9 am 3/2/a", and a video template labeled as street X intersection B is matched in a preset video template library to serve as a corresponding preset template.
In one embodiment, the scoring and sorting the video segments according to the preset video template based on a preset detection model includes: graying each frame of the video clip, calculating the similarity of each frame by similarity calculation of each frame of the video clip and a frame of a preset video template at the same time stamp based on an image similarity calculation model, calculating the similarity of each frame by weighting all the frames of the video clip to obtain the similarity of the video clip and the preset video template, and scoring and sequencing the video clips according to the similarity, wherein the higher the similarity is, the higher the score is, and the higher the score is, the earlier the sequencing is.
Here, the preset threshold may be set according to a specific scenario, for example, in terms of vehicle violation information monitoring, the preset threshold may be set to 95%, and in the event of accident detection, the preset threshold may be set to 80%.
In one embodiment, the determined time evidence graph and the corresponding event type generation interface can be output to the client for display, and the interface information can include the evidence graph, the event time and the event type, for example, when the video source is a video source for a vehicle to run at a crossing B of a street X at 3/2/9 am in 2020, the interface information includes the evidence graph of vehicle line pressing, the line pressing time and the line pressing violation.
Therefore, the intelligent video event detection system 200 provided by the embodiment of the present invention includes: the video acquisition unit 201 is configured to acquire a video source from a database, tag the video source, and determine a corresponding preset video template according to the tag of the video source; a target video determining unit 202, configured to determine a key frame of the video source, split the video source into a plurality of video segments with the same number of frames according to the key frame, score and sort the plurality of video segments according to the preset video template based on a preset detection model, and determine a video segment with a highest score as a target video segment; the detection result determining unit 203 is configured to determine, when the highest score of the scores is greater than or equal to a preset threshold, a frame with the same timestamp in the target video segment and corresponding to a marker frame in the preset video template as an event evidence map, and determine a corresponding event type at the same time. The event detection method has the advantages that the event in the video can be intelligently and automatically detected and the detection result is output, meanwhile, the key image evidence of the event exists in the detection result, on one hand, the event detection efficiency is improved, and on the other hand, the event detection is more accurate and reliable.
It is a further object of an embodiment of the present invention to provide an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to execute the steps of the intelligent video event detection method.
It is yet another object of an embodiment of the present invention to provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, causes the processor to perform the steps of the intelligent video event detection method.
Fig. 3 shows an exemplary system architecture 500 to which the detection method or detection apparatus of an embodiment of the invention may be applied.
As shown in fig. 3, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 serves to provide a medium for communication links between the terminal devices 501, 502, 503 and the server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages or the like. The terminal devices 501, 502, 503 may have various communication client applications installed thereon, such as a shopping application, a web browser application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 501, 502, 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 505 may be a server providing various services, such as a background management server providing support for users to and from messages sent by the terminal devices 501, 502, 503. The background management server can perform analysis and other processing after receiving the terminal device request, and feed back the processing result to the terminal device.
It should be noted that the intelligent video event detection method provided by the embodiment of the present invention may be executed by the server 505, or may be executed by the terminal devices 501, 502, and 503, and accordingly, the intelligent video event detection system may be executed by the server 505, or may be executed by the terminal devices 501, 502, and 503.
It should be understood that the number of terminal devices, networks, and servers in fig. 3 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 4, shown is a block diagram of a computer system 600 suitable for use with the electronic device implementing an embodiment of the present invention. The computer system illustrated in FIG. 4 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the invention.
As shown in fig. 4, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present invention may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a determination unit, an extraction unit, a training unit, and a screening unit. Where the names of these units do not in some cases constitute a limitation on the unit itself, for example, a determination unit may also be described as a "unit that determines a set of candidate users".
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. An intelligent video event detection method, comprising:
acquiring a video source from a database, labeling the video source, and determining a corresponding preset video template according to the label of the video source;
determining key frames of the video source, splitting the video source into a plurality of video clips with the same number of frames according to the key frames, performing scoring and sequencing on the plurality of video clips according to a preset video template based on a preset detection model, and determining the video clip with the highest score as a target video clip;
and when the highest score of the scores is greater than or equal to a preset threshold value, determining frames with the same timestamp in the target video clip and corresponding to the marked frames in the preset video template as an event evidence graph, and simultaneously determining the corresponding event type.
2. The detection method according to claim 1,
the method comprises the following steps of acquiring a video source from a database, labeling the video source, and determining a corresponding preset video template according to the label of the video source, wherein the method comprises the following steps: acquiring a video to be detected from a system database, labeling the video source according to basic information of the video to be detected, and selecting a corresponding preset video template from a preset video template library according to the label of the video source; the video to be detected is the video with the earliest shooting time in all the videos to be detected in the data, and the basic information comprises video shooting position information, video shooting time information and corresponding equipment information of the video shooting device.
3. The detection method according to claim 2, wherein the preset video template comprises a plurality of videos having labels and having the same frame as the target video segment, and the frames of the preset video template are subjected to gray scale processing, and the labels of the preset video template comprise position information.
4. The detection method according to claim 3, wherein the selecting the corresponding preset video template from the preset video template library according to the label of the video source comprises; traversing the preset video template library according to the position information of the video source, and determining a preset video template with the position information identical to that of the video source as a corresponding preset video template.
5. The detection method according to claim 4, wherein the scoring and sorting the plurality of video segments according to the preset video template based on a preset detection model comprises: graying each frame of the video clip, calculating the similarity of each frame by similarity calculation of each frame of the video clip and a frame of a preset video template at the same time stamp based on an image similarity calculation model, calculating the similarity of each frame by weighting all the frames of the video clip to obtain the similarity of the video clip and the preset video template, and scoring and sequencing the video clips according to the similarity, wherein the higher the similarity is, the higher the score is, and the higher the score is, the earlier the sequencing is.
6. An intelligent video event detection system, comprising:
the video acquisition unit is used for acquiring a video source from a database, labeling the video source and determining a corresponding preset video template according to the label of the video source;
the target video determining unit is used for determining key frames of the video source, splitting the video source into a plurality of video clips with the same number of frames according to the key frames, performing scoring and sequencing on the plurality of video clips according to the preset video template based on a preset detection model, and determining the video clip with the highest score as a target video clip;
and the detection result determining unit is used for determining frames with the same timestamp in the target video clip and corresponding to the marked frames in the preset video template as event evidence graphs and determining corresponding event types when the highest score of the scores is greater than or equal to a preset threshold value.
7. The detection system of claim 6, wherein the obtaining a video source from a database, tagging the video source, and determining a corresponding preset video template according to the tag of the video source comprises: acquiring a video to be detected from a system database, labeling the video source according to basic information of the video to be detected, and selecting a corresponding preset video template from a preset video template library according to the label of the video source; the video to be detected is the video with the earliest shooting time in all the videos to be detected in the data, and the basic information comprises video shooting position information, video shooting time information and corresponding equipment information of the video shooting device.
8. The detection system of claim 7,
the preset video template comprises a plurality of videos with labels and the same frame as the target video clip, the frames of the preset video template are subjected to gray processing, and the labels of the preset video template comprise position information.
9. An electronic device, comprising a memory and a processor, the memory having stored therein a computer program that, when executed by the processor, causes the processor to perform the steps of the intelligent video event detection method of any of claims 1 to 5.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, causes the processor to carry out the steps of the intelligent video event detection method according to any one of claims 1 to 5.
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