CN114705178A - Target analysis method based on video track and MAC data - Google Patents
Target analysis method based on video track and MAC data Download PDFInfo
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- CN114705178A CN114705178A CN202110705764.0A CN202110705764A CN114705178A CN 114705178 A CN114705178 A CN 114705178A CN 202110705764 A CN202110705764 A CN 202110705764A CN 114705178 A CN114705178 A CN 114705178A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/343—Calculating itineraries, i.e. routes leading from a starting point to a series of categorical destinations using a global route restraint, round trips, touristic trips
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
- G01C21/3807—Creation or updating of map data characterised by the type of data
- G01C21/3815—Road data
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
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- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
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- Computer Networks & Wireless Communication (AREA)
- Image Analysis (AREA)
Abstract
A target analysis method based on video fusion track and MAC data combines a large amount of monitoring and data acquisition and analysis equipment, and video image structured data application establishes a multidimensional data track database. And analyzing and determining the accurate track of the personnel through multi-dimensional big data and an algorithm, extracting MAC information around the track by combining the track of the personnel, analyzing and determining the MAC address of the target mobile phone. According to the method, the accurate track is precipitated through a large amount of multi-dimensional sensing data, and compared with the reliability of a single-dimensional track, the reliability is higher, and the track is more accurate.
Description
Technical Field
The invention relates to the field of data analysis, in particular to a target analysis method based on video tracks and MAC data.
Background
The MAC address is a physical address solidified in a serial EEPROM on a network card, the identification of an internet site is used for defining the position of network equipment, the MAC address of any network equipment (such as a mobile phone computer) is unique and can not be changed, and the MAC address is just like the identity card number of a resident.
In view of the fact that networks have been integrated into lives of a large number of people, how to quickly and effectively identify and track a target MAC is also very important, however, in the existing target analysis technology, no MAC address is used, and based on this background, the invention aims to provide a target analysis method based on a video track and MAC data.
Disclosure of Invention
In view of the above, the present invention has been made to provide a target analysis method based on video tracks and MAC data that overcomes or at least partially solves the above problems.
In order to solve the technical problem, the embodiment of the application discloses the following technical scheme:
a target analysis method based on video tracks and MAC data comprises the following steps:
s100, collecting data through a standard data interface based on video resources, geographic information and track information;
s200, performing structured analysis on the acquired data to obtain a multi-dimensional database based on video resources, geographic information and track information;
s300, drawing time and space coordinates of the multi-dimensional database to obtain a multi-dimensional track database of the target;
s400, fusing various tracks in a multi-dimensional track library to collide with an accurate multi-dimensional track of a target based on the video track of the target;
s500, performing cyclic comparison on each group of accurate tracks and MAC tracks by using track comparison rules of big data analysis according to a first parameter group based on an accurate target track formed by video tracking and an MAC address track library obtained by extracting peripheral MAC addresses of the video, so as to realize collision analysis of the accurate tracks and the MAC tracks;
s600, relational topology of data can be achieved by using the multi-dimensional front-end sensing data gathered in the video image information base and combining the related system personnel information base and the MAC address base, and tracking of the target is achieved.
Further, the video assets include at least: networking video resources, networking bayonet socket resources, social resources, wifi probe and rail.
Further, in S100, the geographic information at least includes: case geographic information, camera and bayonet geographic information, unmanned aerial vehicle aerial photography information, field exploration geographic information and electric enclosure geographic information.
Further, in S100, the track information at least includes: taxi GPS information, leasing car GPS information, bus IC card track information and mobile phone base station track information.
Further, the specific method of S300 is: the method comprises the steps of extracting a target face characteristic value, a license plate and a behavior characteristic by uploading a target face or a vehicle, carrying out multidimensional tracking on a target by utilizing collected multidimensional data according to the time and the place of the target based on data collected by monitoring equipment, analyzing the space coordinate of the target under a camera coordinate system, carrying out coordinate transformation and proofreading through time, space and the like to obtain a target motion track point set, and forming a target multidimensional track library.
Further, S400 specifically includes: the target is found by checking the video to form a video track of the target, and collision analysis is carried out on the longitude and latitude information of the video track, the face information, the vehicle information and the survey map information in the multidimensional track library to form a more perfect and accurate multidimensional track.
Further, in S500, the first parameter set at least includes: smoothness, distance longitude, matching threshold, correlation interval, error range.
Further, in S500, the collision analysis of the video track and the MAC track includes: extracting retrieval conditions according to the accurate video track, recording the occurrence frequency of each MAC address in a coordinate space, and drawing the MAC address track according to the acquisition time and the existence condition of each MAC address in each point of each scene to form a possible target MAC address track library.
Further, in S500, the collision analysis of the video track and the MAC track further includes: after the collision analysis of the video track and the MAC track, the MAC track route with high track similarity and high time and place matching degree is found out through comparison, and suspected target MAC address information is found out.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
the invention discloses a target analysis method based on video fusion track and MAC data, which is based on video resources, geographic information and track information and collects data through a standard data interface; carrying out structural analysis on the acquired data to obtain a multi-dimensional database based on video resources, geographic information and track information; drawing time and space coordinates of the multi-dimensional database to obtain a multi-dimensional track database of the target; fusing various tracks in a multi-dimensional track library to collide with the accurate multi-dimensional track of the target based on the video track of the target; performing cyclic comparison on each group of video tracks and MAC tracks by using a track comparison rule of big data analysis according to a first parameter group based on a precise target track formed by video tracking and an MAC address track library obtained by extracting peripheral MAC addresses of the video and realizing collision analysis of the precise track and the MAC tracks; the relational topology of data can be realized by using the multidimensional front-end sensing data gathered in the video image information base and combining the related system personnel information base and the MAC address base, and the tracking of the target is realized. According to the method, the accurate track is precipitated through a large amount of multi-dimensional sensing data, and compared with the reliability of a single-dimensional track, the reliability is higher, and the track is more accurate.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a target analysis method based on video tracks and MAC data in embodiment 1 of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In order to solve the problems in the prior art, embodiments of the present invention provide a target analysis method based on a video track and MAC data.
Example 1
The embodiment discloses a target analysis method based on video track and MAC data, as shown in FIG. 1, including:
s100, collecting data through a standard data interface based on video resources, geographic information and track information; specifically, the video resources at least include: networking video resources, networking bayonet socket resources, social resources, wifi probe and rail. The geographic information includes at least: case geographic information, camera and bayonet geographic information, unmanned aerial vehicle aerial photography information, field exploration geographic information and electric enclosure geographic information. The track information at least includes: taxi GPS information, leasing car GPS information, bus IC card track information and mobile phone base station track information.
S200, performing structured analysis on the acquired data to obtain a multi-dimensional database based on video resources, geographic information and track information; specifically, data aggregation and unified interface service are realized through an acquisition system (standard data interface), acquisition equipment (monitoring equipment, an acquisition rod, a bayonet, a social resource point, a WIFI fence probe), data retrieval (time, place, portrait and the like) is realized through the system, multidimensional data (human face, vehicle, video, MAC and the like) in a video image are put in a warehouse according to a standard data format required by relevant departments through structured analysis, and meanwhile, the system is required to support multidimensional retrieval and data analysis comparison.
S300, drawing time and space coordinates of the multi-dimensional database to obtain a multi-dimensional track database of the target; in this embodiment, the specific method of S300 is as follows: the method comprises the steps of extracting a target face characteristic value, a license plate and a behavior characteristic by uploading a target face or a vehicle, carrying out multidimensional tracking on a target by utilizing collected multidimensional data according to the time and the place of the target based on data collected by monitoring equipment, analyzing the space coordinate of the target under a camera coordinate system, carrying out coordinate transformation and proofreading through time, space and the like to obtain a target motion track point set, and forming a target multidimensional track library.
And S400, fusing various tracks in the multi-dimensional track library to collide the accurate multi-dimensional track of the target based on the video track of the target. In this embodiment, S400 specifically includes: the target is found by checking the video, a video track of the target is formed, and collision analysis is carried out on the longitude and latitude information of the video track, the face information, the vehicle information and the current survey image information in the multi-dimensional track library, so that a more perfect and accurate multi-dimensional track is formed.
S500, performing cyclic comparison on each group of accurate tracks and MAC tracks by using track comparison rules of big data analysis according to a first parameter group based on an accurate target track formed by video tracking and an MAC address track library obtained by extracting peripheral MAC addresses of the video, so as to realize collision analysis of the accurate tracks and the MAC tracks;
in this embodiment, the collision analysis of the video track and the MAC track includes: extracting retrieval conditions according to the accurate video track, recording the occurrence frequency of each MAC address in a coordinate space, and drawing an MAC address track according to the acquisition time and the existence condition of each MAC address in each point of each scene to form a possible target MAC address track library.
In S500 of this embodiment, the collision analysis of the video track and the MAC track further includes: after the collision analysis of the video track and the MAC track, the MAC track route with high track similarity and high time and place matching degree is found out through comparison, and suspected target MAC address information is found out.
S600, relational topology of data can be achieved by means of the multi-dimensional front-end sensing data gathered in the video image information base and the combination of the related system personnel information base and the MAC address base, and tracking of the target is achieved. Specifically, based on an accurate target track formed by video tracking, each group of video tracks and MAC tracks are circularly compared with an MAC address track library obtained by extracting MAC addresses around the video and according to smoothness, distance longitude, a matching threshold, an association interval and an error range and by using a track comparison rule of big data analysis, collision analysis of the video tracks and the MAC tracks is realized, an MAC track route with high track similarity and high time and place matching degree is found out through comparison, and suspected target MAC address information is found out.
The embodiment discloses a target analysis method based on video fusion track and MAC data, which is based on video resources, geographic information and track information and collects data through a standard data interface; carrying out structural analysis on the acquired data to obtain a multidimensional database based on video resources, geographic information and track information; drawing time and space coordinates of the multi-dimensional database to obtain a multi-dimensional track database of the target; fusing various tracks in a multi-dimensional track library to collide with the accurate multi-dimensional track of the target based on the video track of the target; performing cyclic comparison on each group of video tracks and MAC tracks by using a track comparison rule of big data analysis according to a first parameter group based on a precise target track formed by video tracking and an MAC address track library obtained by extracting peripheral MAC addresses of the video and realizing collision analysis of the precise track and the MAC tracks; the relational topology of data can be realized by using the multidimensional front-end sensing data gathered in the video image information base and combining the related system personnel information base and the MAC address base, and the tracking of the target is realized. According to the method, the accurate track is precipitated through a large amount of multi-dimensional sensing data, and compared with the reliability of a single-dimensional track, the reliability is higher, and the track is more accurate.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. 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 disclosure.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Of course, the processor and the storage medium may reside as discrete components in a user terminal.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in memory units and executed by processors. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
Claims (9)
1. A target analysis method based on video track and MAC data is characterized by comprising the following steps:
s100, collecting data through a standard data interface based on video resources, geographic information and track information;
s200, performing structured analysis on the acquired data to obtain a multi-dimensional database based on video resources, geographic information and track information;
s300, drawing time and space coordinates of the multi-dimensional database to obtain a multi-dimensional track database of the target;
s400, fusing various tracks in a multi-dimensional track library to collide with an accurate multi-dimensional track of a target based on the video track of the target;
s500, performing cyclic comparison on each group of accurate tracks and MAC tracks by using track comparison rules of big data analysis according to a first parameter group based on an accurate target track formed by video tracking and an MAC address track library obtained by extracting peripheral MAC addresses of the video, so as to realize collision analysis of the accurate tracks and the MAC tracks;
s600, relational topology of data can be achieved by means of the multi-dimensional front-end sensing data gathered in the video image information base and the combination of the related system personnel information base and the MAC address base, and tracking of the target is achieved.
2. The method for target analysis based on video track and MAC data as claimed in claim 1, wherein in S100, the video resources at least include: networking video resources, networking bayonet socket resources, social resources, wifi probe and rail.
3. The method for target analysis based on video track and MAC data as claimed in claim 1, wherein in S100, the geographic information at least includes: case geographic information, camera and bayonet geographic information, unmanned aerial vehicle aerial photography information, field exploration geographic information and electric enclosure geographic information.
4. The method for analyzing a target according to claim 1, wherein in S100, the track information at least includes: taxi GPS information, leasing car GPS information, bus IC card track information and mobile phone base station track information.
5. The method for analyzing the target based on the video track and the MAC data as claimed in claim 1, wherein the specific method of S300 is: the method comprises the steps of extracting a target face characteristic value, a license plate and a behavior characteristic by uploading a target face or a vehicle, carrying out multidimensional tracking on a target by utilizing collected multidimensional data according to the time and the place of the target based on data collected by monitoring equipment, analyzing the space coordinate of the target under a camera coordinate system, carrying out coordinate transformation and proofreading through time, space and the like to obtain a target motion track point set, and forming a target multidimensional track library.
6. The method for target analysis based on video track and MAC data as claimed in claim 1, wherein S400 specifically includes: the target is found by checking the video to form a video track of the target, and collision analysis is carried out on the longitude and latitude information of the video track, the face information, the vehicle information and the survey map information in the multidimensional track library to form a more perfect and accurate multidimensional track.
7. The method of claim 1, wherein in S500, the first parameter set at least comprises: smoothness, distance longitude, matching threshold, correlation interval, error range.
8. The method for analyzing the target based on the video track and the MAC data as claimed in claim 1, wherein in S500, the collision analysis of the video track and the MAC track comprises: extracting retrieval conditions according to the accurate video track, recording the occurrence frequency of each MAC address in a coordinate space, and drawing the MAC address track according to the acquisition time and the existence condition of each MAC address in each point of each scene to form a possible target MAC address track library.
9. The method for analyzing the target based on the video track and the MAC data as claimed in claim 1, wherein the collision analysis of the video track and the MAC track in S500 further comprises: after the collision analysis of the video track and the MAC track, the MAC track route with high track similarity and high time and place matching degree is found out through comparison, and suspected target MAC address information is found out.
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| CN202110705764.0A CN114705178A (en) | 2021-06-24 | 2021-06-24 | Target analysis method based on video track and MAC data |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115409869A (en) * | 2022-09-05 | 2022-11-29 | 北京拙河科技有限公司 | Snow field trajectory analysis method and device based on MAC tracking |
| CN117454199A (en) * | 2023-12-20 | 2024-01-26 | 北京数原数字化城市研究中心 | Track association method, system, electronic device and readable storage medium |
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2021
- 2021-06-24 CN CN202110705764.0A patent/CN114705178A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115409869A (en) * | 2022-09-05 | 2022-11-29 | 北京拙河科技有限公司 | Snow field trajectory analysis method and device based on MAC tracking |
| CN117454199A (en) * | 2023-12-20 | 2024-01-26 | 北京数原数字化城市研究中心 | Track association method, system, electronic device and readable storage medium |
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