CN117278697A - System and method for collecting panoramic video of automobile collision - Google Patents
System and method for collecting panoramic video of automobile collision Download PDFInfo
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- CN117278697A CN117278697A CN202311549354.7A CN202311549354A CN117278697A CN 117278697 A CN117278697 A CN 117278697A CN 202311549354 A CN202311549354 A CN 202311549354A CN 117278697 A CN117278697 A CN 117278697A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/222—Studio circuitry; Studio devices; Studio equipment
- H04N5/262—Studio circuits, e.g. for mixing, switching-over, change of character of image, other special effects ; Cameras specially adapted for the electronic generation of special effects
- H04N5/2624—Studio circuits, e.g. for mixing, switching-over, change of character of image, other special effects ; Cameras specially adapted for the electronic generation of special effects for obtaining an image which is composed of whole input images, e.g. splitscreen
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
- G06V10/225—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition based on a marking or identifier characterising the area
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- 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
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
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- G06V20/44—Event detection
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- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
- G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
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- G—PHYSICS
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/57—Mechanical or electrical details of cameras or camera modules specially adapted for being embedded in other devices
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- H—ELECTRICITY
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Abstract
The invention is applicable to the technical field of image communication, and provides a system and a method for acquiring panoramic video of automobile collision, wherein the method comprises the following steps: acquiring a plurality of wide-angle video data, wherein the wide-angle video data are obtained by intelligent probes arranged on the automobile body in a plurality of directions; the wide-angle video data are spliced to obtain panoramic video data; performing collision feature recognition on panoramic video data content, and generating a marking instruction to time mark the panoramic video data when collision occurs; setting a tracing time threshold, and intercepting panoramic video data according to the time mark of the panoramic video data and the tracing time threshold to obtain a panoramic collision image; and storing and uploading the panoramic collision image to the cloud. The panoramic collision image is utilized to completely restore the collision process of the automobile, and the route and the surrounding conditions of the automobile in the whole process can be clearly observed, so that the behavior judgment of the automobile body is facilitated.
Description
Technical Field
The invention relates to the technical field of image communication, in particular to a system and a method for acquiring panoramic video of automobile collision.
Background
Automobiles are indispensable transportation means for modern people to travel, and it is well known that accidents caused by collisions when the automobiles travel on roads cannot be completely avoided.
When an automobile collides, the judgment on the aspect of accident identification is necessarily involved, if an image is involved in the collision, the judgment is very beneficial to the recovery and the accident judgment in the collision process, and if the image of the collision is not recorded, the monitoring video on a road is required to be called, so that the whole process is complicated.
When many automobiles are provided with an automobile data recorder, the automobile data recorder can record images before and after the automobile data recorder, namely, the automobile data recorder can record images when the automobile data recorder collides before and after the automobile data recorder, but because the view angle is narrow, the automobile data recorder cannot well restore the collision process, such as the traveling track of the automobile, the information of the cause of the collision and the like, and the side collision condition cannot be recorded, so that the existing collision recording mode is unclear and clear in the restoration process, the accident judgment can be influenced, and therefore, the system and the method for acquiring the panoramic video of the automobile collision are provided, and the problem is solved.
Disclosure of Invention
In view of the shortcomings of the prior art, the present invention aims to provide a system and a method for capturing panoramic video of an automobile collision, so as to solve the problems in the prior art.
The invention is realized in that a method for collecting panoramic video of automobile collision comprises the following steps:
acquiring a plurality of wide-angle video data, wherein the wide-angle video data are obtained by intelligent probes arranged on the automobile body in a plurality of directions;
the wide-angle video data are spliced to obtain panoramic video data;
performing collision feature recognition on panoramic video data content, and generating a marking instruction to time mark the panoramic video data when collision occurs;
setting a tracing time threshold, and intercepting panoramic video data according to the time mark of the panoramic video data and the tracing time threshold to obtain a panoramic collision image;
and storing and uploading the panoramic collision image to the cloud.
As a further scheme of the invention: the step of performing collision feature recognition on panoramic video data content specifically comprises the following steps:
processing and converting according to panoramic video data and storing the panoramic video data as aerial view image data;
performing collision recognition on the automobile body according to the aerial view image data, and calibrating a time node of the aerial view image data when the automobile body collides with an object;
and generating a marking instruction according to the time node of the aerial view image data to time mark the panoramic video data.
As a further scheme of the invention: the step of performing collision recognition on the automobile body according to the bird's eye view image data specifically comprises the following steps:
performing frame selection on the automobile body according to the aerial view image data, and marking the outline of the automobile body according to a frame selection result to obtain an automobile body outline;
marking other objects according to the aerial view image data to obtain object contour lines, wherein the other objects are the other objects except the automobile body in the aerial view image;
when the vehicle body contour line is in contact with or intersects with the object contour line, the collision is judged, and a marking instruction is generated.
As a further scheme of the invention: the step of performing time marking on panoramic video data according to a time node generation marking instruction of the bird's eye view image data specifically comprises the following steps:
when a marking instruction is obtained, generating a patrol instruction, wherein the patrol instruction is used for checking a collision sensor arranged on an automobile body;
verifying a result of collision recognition on the automobile body according to the bird's-eye view image data according to the inspection result;
when the verification is passed, the marking instruction time marks the panoramic video data.
As a further scheme of the invention: the step of intercepting panoramic video data according to the time mark and the tracing time threshold value of the panoramic video data to obtain panoramic collision images specifically comprises the following steps:
setting a video time rail, and applying panoramic video data to the video time rail;
calibrating the video time rail according to the time mark, and editing panoramic video data according to the tracing time threshold and the calibrated video time rail to obtain a panoramic collision image;
and deleting the rest panoramic video data excluding the panoramic collision image on the video time rail.
As a further scheme of the invention: the step of storing and uploading the panoramic collision image to the cloud specifically comprises the following steps:
obtaining marking time information according to the generation time of the panoramic collision image, obtaining position information based on communication equipment on an automobile body, and obtaining license plate information of the automobile according to the panoramic video data;
generating file name information based on the tag time information, the location information, and the license plate information;
and integrating the file name information and the panoramic collision image, storing and uploading the file name information and the panoramic collision image to the cloud end, so that the name of the panoramic collision image can reflect the time and the position of collision and the license plate of the vehicle.
Another object of the present invention is to provide a system for car crash panoramic video acquisition, the system comprising:
the video data acquisition module acquires a plurality of wide-angle video data, wherein the wide-angle video data are obtained by intelligent probes arranged on the automobile body in a plurality of directions;
the video data processing module is used for splicing the wide-angle video data to obtain panoramic video data;
the collision recognition module is used for performing collision feature recognition on the panoramic video data content, and generating a marking instruction to time mark the panoramic video data when collision occurs;
the video data editing module is used for setting a tracing time threshold value, and intercepting panoramic video data according to the time mark of the panoramic video data and the tracing time threshold value to obtain a panoramic collision image;
and the panoramic collision image uploading module is used for storing and uploading the panoramic collision image to the cloud.
As a further scheme of the invention: the collision recognition module includes:
the video conversion unit processes and converts the panoramic video data and stores the panoramic video data as bird's-eye view image data;
the aerial view image processing unit is used for carrying out collision recognition on the automobile body according to the aerial view image data, and calibrating a time node of the aerial view image data when the automobile body collides with an object;
and the time marking unit is used for time marking the panoramic video data according to a time node generation marking instruction of the aerial view image data.
As a further scheme of the invention: the video data editing module includes:
the time rail generation unit is used for setting a video time rail and applying panoramic video data to the video time rail;
the video editing unit is used for calibrating the video time rail according to the time mark, editing the panoramic video data according to the traceability time threshold and the calibrated video time rail to obtain a panoramic collision image;
and the data deleting unit deletes the rest panoramic video data of which the panoramic collision image is removed on the video time rail.
As a further scheme of the invention: the panorama collision image uploading module comprises:
an additional information acquisition unit for acquiring tag time information according to the generation time of the panoramic collision image, acquiring position information based on communication equipment on the automobile body, and acquiring license plate information of the automobile according to the panoramic video data;
an additional information processing unit that generates file name information based on the tag time information, the location information, and the license plate information;
and the data integration unit integrates file name information and the panoramic collision image, stores the file name information and the panoramic collision image and uploads the file name information and the panoramic collision image to the cloud end, so that the name of the panoramic collision image can reflect the time and the position of collision and license plates of vehicles.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, a plurality of wide-angle video data can be acquired according to the intelligent probes arranged on the automobile body in a plurality of directions, the wide-angle video data is sheared and spliced to obtain the panoramic video data, the collision feature recognition is carried out based on the panoramic video data, when collision occurs, the panoramic video data is selectively intercepted to serve as a panoramic collision image, the panoramic collision image is stored and uploaded to the cloud, due to the characteristics of the panoramic video, the details of the collision can be more intuitively known, the panoramic collision image is traced forward, the acquired panoramic collision image can completely restore the automobile collision process, the route and the surrounding situation of an automobile in the whole process can be clearly observed, the judgment of the behavior of the automobile body is facilitated, and in conclusion, the effects of all aspects can be more favorable for the judgment of the accident in the later stage of the collision.
Drawings
Fig. 1 is a flow chart of a method for vehicle crash panoramic video acquisition.
Fig. 2 is a flow chart of collision feature recognition of panoramic video data content in a method for vehicle collision panoramic video acquisition.
Fig. 3 is a flowchart of collision recognition of an automobile body according to bird's eye view image data in a method for collecting panoramic video of an automobile collision.
Fig. 4 is a flowchart of a method for acquiring panoramic video data of an automobile collision, wherein the method is used for performing time marking on panoramic video data according to a time node generation marking instruction of bird's-eye view image data.
Fig. 5 is a flowchart of a method for acquiring panoramic video of an automobile collision, wherein the method is used for capturing panoramic video data according to a time stamp and a tracing time threshold of the panoramic video data to obtain panoramic collision images.
Fig. 6 is a flowchart of a method for capturing panoramic video of an automobile collision, wherein panoramic collision images are stored and uploaded to a cloud.
Fig. 7 is a schematic structural diagram of a system for vehicle collision panoramic video acquisition.
Fig. 8 is a schematic structural diagram of a collision recognition module in a system for vehicle collision panoramic video acquisition.
Fig. 9 is a schematic structural diagram of a video data editing module in a system for vehicle collision panoramic video acquisition.
Fig. 10 is a schematic structural diagram of a panoramic collision image uploading module in a system for capturing panoramic video of an automobile collision.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clear, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Specific implementations of the invention are described in detail below in connection with specific embodiments.
As shown in fig. 1, an embodiment of the present invention provides a method for capturing panoramic video of an automobile collision, the method comprising the steps of:
s100, acquiring a plurality of wide-angle video data, wherein the wide-angle video data are obtained by intelligent probes arranged on an automobile body in a plurality of directions;
s200, splicing the wide-angle video data to obtain panoramic video data;
s300, collision feature recognition is carried out on panoramic video data content, and when collision occurs, a marking instruction is generated to carry out time marking on the panoramic video data;
s400, setting a tracing time threshold, and intercepting panoramic video data according to the time mark of the panoramic video data and the tracing time threshold to obtain a panoramic collision image;
s500, the panoramic collision image is stored and uploaded to the cloud.
It should be noted that panoramic images are applied to a plurality of existing automobiles, but most of panoramic images are used in the aspects of automatic parking, reversing and the like, mainly for a driver to more accurately operate the automobiles to move in a limited space, a plurality of intelligent probes for collecting information are generally arranged on the automobile body of the automobile with the panoramic images, all conditions around the automobile body can be recorded by the intelligent probes, and the step of splicing wide-angle video data to obtain panoramic video data adopts the existing video splicing technology.
According to the embodiment of the invention, a plurality of wide-angle video data can be acquired according to the intelligent probes arranged on the automobile body in a plurality of directions, panoramic video data can be obtained by shearing and splicing the wide-angle video data, collision feature recognition is carried out based on the panoramic video data, when a collision occurs, a section of panoramic video data can be selectively intercepted as a panoramic collision image, the panoramic collision image is stored and uploaded to the cloud, the details of the collision can be more intuitively known due to the characteristics of the panoramic video, the panoramic collision image is traced forward, the acquired panoramic collision image can completely restore the collision process of the automobile, the route and the surrounding situation of the automobile in the whole process can be clearly observed, the judgment of the behavior of the automobile body is facilitated, and in conclusion, the effects of all aspects can be more favorable for the judgment of the accident in the later stage of the collision.
As shown in fig. 2, as a preferred embodiment of the present invention, the step of performing collision feature recognition on panoramic video data content specifically includes:
s301, processing and converting according to panoramic video data and storing the panoramic video data as bird' S-eye view image data;
s302, collision recognition is carried out on the automobile body according to the aerial view image data, and when the automobile body collides with an object, the time node of the aerial view image data is calibrated;
s303, performing time marking on the panoramic video data according to a time node generation marking instruction of the aerial view image data.
In the embodiment of the invention, the analysis processing of the collision by using the panoramic video is a complex process, which possibly involves the judgment of the distance, and the sensor of the vehicle body is required to verify the signal generated by the collision, and the process requires more data processing processes, but the collision can be identified by the bird's eye view angle in the panoramic video, the panoramic video data is processed and transformed and stored as bird's eye view image data, and the collision is identified by the bird's eye view angle more directly and quickly.
As shown in fig. 3, as a preferred embodiment of the present invention, the step of performing collision recognition on the automobile body according to the bird's-eye view image data specifically includes:
s3021, performing frame selection on an automobile body according to bird' S-eye view image data, and marking the outline of the automobile body according to a frame selection result to obtain an automobile body outline;
s3022, marking other objects according to the aerial view image data to obtain object contour lines, wherein the other objects are the other objects except the automobile body in the aerial view image;
s3023, when the vehicle body contour line contacts or intersects the object contour line, judging that a collision occurs, and generating a marking instruction.
According to the embodiment of the invention, the car body is framed according to the aerial view image data and then the outline is marked, so that the car body outline can be obtained, and meanwhile, the outline is marked on other objects, when the car body outline is contacted or intersected with the object outline, the collision is meant to occur, and the collision situation can be more accurately identified through the mode.
As shown in fig. 4, as a preferred embodiment of the present invention, the step of time-stamping panoramic video data according to the time node generation marking instruction of the bird's eye view image data specifically includes:
s3031, when a marking instruction is obtained, generating a patrol instruction, wherein the patrol instruction is used for checking a collision sensor arranged on an automobile body;
s3032, verifying a result of collision recognition on the automobile body according to the aerial view image data according to the inspection result;
and S3033, when the verification is passed, the marking instruction marks the time of the panoramic video data.
In the embodiment of the invention, a certain error may exist in a video recognition mode generally, particularly in the case of high-speed movement of an automobile, the video recognition mode cannot completely ensure accuracy, but the embodiment can verify through a collision sensor arranged on the automobile body, wherein the collision sensor is the existing technology of the automobile, and the accuracy of a panoramic video recognition collision result is verified through a signal uploaded by the collision sensor, so that the panoramic collision image is truly, effectively and effectively available.
As shown in fig. 5, as a preferred embodiment of the present invention, the step of intercepting panoramic video data according to the time stamp and the tracing time threshold of the panoramic video data to obtain a panoramic collision image specifically includes:
s401, setting a video time rail, and applying panoramic video data to the video time rail;
s402, calibrating a video time rail according to the time mark, and editing panoramic video data according to a tracing time threshold and the calibrated video time rail to obtain a panoramic collision image;
s403, deleting the rest panoramic video data of the panoramic collision image removed on the video time track.
In the embodiment of the invention, a mode of processing panoramic video data is specifically introduced, the time of quantifying the panoramic video can be realized by setting the time rail, and the length of the video can be determined according to the traceability time threshold, so that the panoramic video data is clipped to obtain a corresponding panoramic collision image, and meanwhile, the rest useless panoramic video data is deleted, so that the pressure of storage can be reduced.
As shown in fig. 6, as a preferred embodiment of the present invention, the step of saving and uploading the panoramic collision image to the cloud specifically includes:
s501, obtaining marking time information according to the generation time of panoramic collision images, obtaining position information based on communication equipment on an automobile body, and obtaining license plate information of the automobile according to the panoramic video data;
s502, generating file name information based on the mark time information, the position information and the license plate information;
s503, integrating file name information and the panoramic collision image to store and upload to the cloud, so that the name of the panoramic collision image can reflect the time and position of collision and license plate of the vehicle.
In the embodiment of the invention, a process of storing the panoramic collision image is specifically introduced, the marking time information and the position information are relatively easy to obtain, the prior art can realize, license plate information is obtained by characteristic recognition through panoramic video data, and after the information is integrated to the file name of the stored panoramic collision image, the information can be more conveniently and subsequently searched, and meanwhile, when the collision process is recorded, some basic conditions are more intuitively displayed.
As shown in fig. 7, an embodiment of the present invention further provides a system for capturing panoramic video of an automobile collision, where the system includes:
the video data acquisition module 100 acquires a plurality of wide-angle video data, which are obtained by intelligent probes arranged on the automobile body in a plurality of directions;
the video data processing module 200 performs splicing processing on the wide-angle video data to obtain panoramic video data;
the collision recognition module 300 is used for performing collision feature recognition on the panoramic video data content, and generating a marking instruction to time mark the panoramic video data when collision occurs;
the video data editing module 400 sets a tracing time threshold, and intercepts panoramic video data according to the time mark and the tracing time threshold of the panoramic video data to obtain a panoramic collision image;
the panoramic collision image uploading module 500 stores and uploads the panoramic collision image to the cloud.
According to the embodiment of the invention, a plurality of wide-angle video data can be acquired according to the intelligent probes arranged on the automobile body in a plurality of directions, panoramic video data can be obtained by shearing and splicing the wide-angle video data, collision feature recognition is carried out based on the panoramic video data, when a collision occurs, a section of panoramic video data can be selectively intercepted as a panoramic collision image, the panoramic collision image is stored and uploaded to the cloud, the details of the collision can be more intuitively known due to the characteristics of the panoramic video, the panoramic collision image is traced forward, the acquired panoramic collision image can completely restore the collision process of the automobile, the route and the surrounding situation of the automobile in the whole process can be clearly observed, the judgment of the behavior of the automobile body is facilitated, and in conclusion, the effects of all aspects can be more favorable for the judgment of the accident in the later stage of the collision.
As shown in fig. 8, as a preferred embodiment of the present invention, the collision recognition module 300 includes:
a video conversion unit 301 that performs processing conversion based on panoramic video data and stores the processed panoramic video data as bird's-eye view image data;
the bird's-eye view image processing unit 302 performs collision recognition on the automobile body according to the bird's-eye view image data, and calibrates a time node of the bird's-eye view image data when the automobile body collides with an object;
the time marking unit 303 time-marks the panoramic video data according to the time node generation marking instruction of the bird's-eye view image data.
As shown in fig. 9, as a preferred embodiment of the present invention, the video data editing module 400 includes:
a time track generation unit 401, which sets a video time track and applies panoramic video data to the video time track;
the video editing unit 402 is used for calibrating the video time rail according to the time mark, editing the panoramic video data according to the traceability time threshold and the calibrated video time rail to obtain a panoramic collision image;
the data deleting unit 403 deletes the remaining panoramic video data excluding the panoramic impact image on the video time track. As shown in fig. 10, as a preferred embodiment of the present invention, the panorama impact image uploading module 500 includes:
an additional information acquisition unit 501 for acquiring tag time information according to the generation time of the panoramic collision image, acquiring position information based on communication equipment on the automobile body, and acquiring license plate information of the vehicle according to the panoramic video data;
an additional information processing unit 502 that generates file name information based on the tag time information, the position information, and the license plate information;
the data integrating unit 503 integrates the file name information and the panoramic crash image, and stores and uploads the file name information and the panoramic crash image to the cloud end, so that the name of the panoramic crash image can reflect the time and the position of the crash and the license plate of the vehicle.
The foregoing description of the preferred embodiments of the present invention should not be taken as limiting the invention, but rather should be understood to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A method for car crash panoramic video acquisition, the method comprising the steps of:
acquiring a plurality of wide-angle video data, wherein the wide-angle video data are obtained by intelligent probes arranged on the automobile body in a plurality of directions;
the wide-angle video data are spliced to obtain panoramic video data;
performing collision feature recognition on panoramic video data content, and generating a marking instruction to time mark the panoramic video data when collision occurs;
setting a tracing time threshold, and intercepting panoramic video data according to the time mark of the panoramic video data and the tracing time threshold to obtain a panoramic collision image;
and storing and uploading the panoramic collision image to the cloud.
2. The method for capturing panoramic video of an automobile crash according to claim 1, wherein said step of identifying the crash features of the panoramic video data content comprises:
processing and converting according to panoramic video data and storing the panoramic video data as aerial view image data;
performing collision recognition on the automobile body according to the aerial view image data, and calibrating a time node of the aerial view image data when the automobile body collides with an object;
and generating a marking instruction according to the time node of the aerial view image data to time mark the panoramic video data.
3. The method for capturing panoramic video of an automobile crash according to claim 2, wherein said step of performing crash recognition on the automobile body according to the bird's-eye view image data specifically comprises:
performing frame selection on the automobile body according to the aerial view image data, and marking the outline of the automobile body according to a frame selection result to obtain an automobile body outline;
marking other objects according to the aerial view image data to obtain object contour lines, wherein the other objects are the other objects except the automobile body in the aerial view image;
when the vehicle body contour line is in contact with or intersects with the object contour line, the collision is judged, and a marking instruction is generated.
4. The method for capturing panoramic video for an automobile crash according to claim 2, wherein the step of time-stamping panoramic video data according to the time node generation marking instruction of the bird's-eye view image data specifically comprises:
when a marking instruction is obtained, generating a patrol instruction, wherein the patrol instruction is used for checking a collision sensor arranged on an automobile body;
verifying a result of collision recognition on the automobile body according to the bird's-eye view image data according to the inspection result;
when the verification is passed, the marking instruction time marks the panoramic video data.
5. The method for capturing panoramic video of an automobile crash according to claim 1, wherein the step of capturing panoramic video data according to a time stamp and a tracing time threshold of the panoramic video data to obtain a panoramic crash image specifically comprises:
setting a video time rail, and applying panoramic video data to the video time rail;
calibrating the video time rail according to the time mark, and editing panoramic video data according to the tracing time threshold and the calibrated video time rail to obtain a panoramic collision image;
and deleting the rest panoramic video data excluding the panoramic collision image on the video time rail.
6. The method for capturing panoramic video of an automobile crash according to claim 1, wherein the step of saving and uploading the panoramic crash image to the cloud comprises:
obtaining marking time information according to the generation time of the panoramic collision image, obtaining position information based on communication equipment on an automobile body, and obtaining license plate information of the automobile according to the panoramic video data;
generating file name information based on the tag time information, the location information, and the license plate information;
and integrating the file name information and the panoramic collision image, storing and uploading the file name information and the panoramic collision image to the cloud end, so that the name of the panoramic collision image can reflect the time and the position of collision and the license plate of the vehicle.
7. A system for vehicle crash panoramic video acquisition, the system comprising:
the video data acquisition module acquires a plurality of wide-angle video data, wherein the wide-angle video data are obtained by intelligent probes arranged on the automobile body in a plurality of directions;
the video data processing module is used for splicing the wide-angle video data to obtain panoramic video data;
the collision recognition module is used for performing collision feature recognition on the panoramic video data content, and generating a marking instruction to time mark the panoramic video data when collision occurs;
the video data editing module is used for setting a tracing time threshold value, and intercepting panoramic video data according to the time mark of the panoramic video data and the tracing time threshold value to obtain a panoramic collision image;
and the panoramic collision image uploading module is used for storing and uploading the panoramic collision image to the cloud.
8. The system for vehicle crash panoramic video acquisition as recited in claim 7, wherein said crash identification module comprises:
the video conversion unit processes and converts the panoramic video data and stores the panoramic video data as bird's-eye view image data;
the aerial view image processing unit is used for carrying out collision recognition on the automobile body according to the aerial view image data, and calibrating a time node of the aerial view image data when the automobile body collides with an object;
and the time marking unit is used for time marking the panoramic video data according to a time node generation marking instruction of the aerial view image data.
9. The system for vehicle crash panoramic video collection as recited in claim 7, wherein said video data editing module comprises:
the time rail generation unit is used for setting a video time rail and applying panoramic video data to the video time rail;
the video editing unit is used for calibrating the video time rail according to the time mark, editing the panoramic video data according to the traceability time threshold and the calibrated video time rail to obtain a panoramic collision image;
and the data deleting unit deletes the rest panoramic video data of which the panoramic collision image is removed on the video time rail.
10. The system for vehicle crash panoramic video collection as recited in claim 7, wherein said panoramic crash image upload module comprises:
an additional information acquisition unit for acquiring tag time information according to the generation time of the panoramic collision image, acquiring position information based on communication equipment on the automobile body, and acquiring license plate information of the automobile according to the panoramic video data;
an additional information processing unit that generates file name information based on the tag time information, the location information, and the license plate information;
and the data integration unit integrates file name information and the panoramic collision image, stores the file name information and the panoramic collision image and uploads the file name information and the panoramic collision image to the cloud end, so that the name of the panoramic collision image can reflect the time and the position of collision and license plates of vehicles.
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