CN116860904B - Target tracking method and device based on decentralization network - Google Patents
Target tracking method and device based on decentralization network Download PDFInfo
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Abstract
The invention relates to a target tracking method and device based on a decentralization network, wherein the method comprises the steps of responding to an acquired tracking object, acquiring related information of the tracking object, drawing a position point group of the tracking object according to the related information, and marking the position point group as a first position point group; selecting a plurality of position points from the first position point group; acquiring suspected objects appearing at each position point; and merging the suspected objects, drawing a position point group of the suspected objects according to the merging result, marking the position point group as a second position point group, calculating the coincidence degree of the first position point group and the second position point group, and screening the suspected objects according to the coincidence degree result to obtain the suspected objects associated with the tracked objects. According to the target tracking method and device based on the decentralization network, the accuracy of the result in the process of finding and tracking the target is improved by using the position association degree analysis and the time-space association degree analysis.
Description
Technical Field
The invention relates to the technical field of big data processing, in particular to a target tracking method and device based on a decentralization network.
Background
Target tracking has application prospects in multiple fields, and a specific tracking mode is to analyze association relation of the target tracking according to data left by a tracked object. In the conventional tracking method, the processing is performed by means of inquiring and using data, and the processing requires a sufficient hand and processing time, and has time lag.
With the advent of big data applications, techniques for analyzing and tracking targets using big data began to be used, which has the advantage that no human hand is needed, but further research is needed in how the data is used to obtain results and the accuracy of the results.
Disclosure of Invention
The invention provides a target tracking method and device based on a decentralization network, which improve the accuracy of results in the process of finding and tracking targets by using a position association analysis and a time-space association analysis.
The above object of the present invention is achieved by the following technical solutions:
in a first aspect, the present invention provides a target tracking method based on a decentralized network, including:
Responding to the acquired tracking object, acquiring related information of the tracking object, drawing a position point group of the tracking object according to the related information, and marking the position point group as a first position point group;
Selecting a plurality of position points from the first position point group;
acquiring suspected objects appearing at each position point;
Combining the suspected objects, drawing a position point group of the suspected objects according to the combination result, and marking the position point group as a second position point group; and
And calculating the coincidence degree of the first position point group and the second position point group, and screening the suspected objects according to the coincidence degree result to obtain the suspected objects associated with the tracked objects.
In one possible implementation manner of the present invention, the position points are selected on the first position point group for multiple times, and the position point in each selection process is not completely overlapped with the position point in the last selection process.
In one possible implementation manner of the invention, the suspected objects are screened for one time according to the coincidence degree of the first position point group and the second position point group;
And carrying out secondary screening on the suspected objects according to the occurrence times of the suspected objects when the position points are selected for multiple times.
In one possible implementation of the present invention, calculating the overlap ratio of the first position point set and the second position point set includes:
Selecting the first position point group according to the time point of the starting position point and the time point of the stopping position point of the second position point group;
Counting the number of the same position points on the intercepted first position point group and the second position point group, and recording the number as a first number; and
The first number of duty cycles in all location points on the truncated first set of location points is calculated.
In one possible implementation manner of the present invention, when the number of location points in the first location point group and the second location point group is different, the method further includes:
arranging and connecting the position points in the first position point group and the second position point group on a time sequence, and carrying out deletion processing on the default position points when connecting;
Calculating the area of a connecting line between the substituted connecting line of the missing processing area and the corresponding position point; and
And judging whether a coincident position point exists between the vacant section and the corresponding first position point group according to the area and the length of the vacant section.
In one possible implementation manner of the present invention, the method further includes:
Calculating the distance between the vacant section and the corresponding first position point group; and
Discarding the part of the vacant section exceeding the allowable distance;
the method comprises the steps of dividing a blank section and a corresponding first position point group by using a plurality of parallel line segments.
In one possible implementation manner of the present invention, the method further includes filling the empty segment, and filling the empty segment includes:
acquiring a starting position point and a cut-off position point of a vacant section;
obtaining a plurality of paths according to the positions of the starting position point and the stopping position point on the map; and
Filling the blank section by using a path;
The path selects or selects a shortest path according to the suspected object.
In a second aspect, the present invention provides a target tracking apparatus comprising:
the first processing unit is used for responding to the acquired tracking object, acquiring the related information of the tracking object, drawing a position point group of the tracking object according to the related information and marking the position point group as a first position point group;
The first selecting unit is used for selecting a plurality of position points in the first position point group;
The second selecting unit is used for acquiring suspected objects appearing at each position point;
the second processing unit is used for merging the suspected objects, drawing a position point group of the suspected objects according to the merging result and marking the position point group as a second position point group; and
And the screening unit is used for calculating the coincidence ratio of the first position point group and the second position point group and screening the suspected objects according to the coincidence ratio result to obtain the suspected objects associated with the tracked objects.
In a third aspect, the present invention provides a target tracking system, the system comprising:
one or more memories for storing instructions; and
One or more processors configured to invoke and execute the instructions from the memory, to perform the method as described in the first aspect and any possible implementation of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes:
A program which, when executed by a processor, performs a method as described in the first aspect and any possible implementation of the first aspect.
In a fifth aspect, the present invention provides a computer program product comprising program instructions which, when executed by a computing device, perform a method as described in the first aspect and any possible implementation of the first aspect.
In a sixth aspect, the present invention provides a chip system comprising a processor for implementing the functions involved in the above aspects, e.g. generating, receiving, transmitting, or processing data and/or information involved in the above methods.
The chip system can be composed of chips, and can also comprise chips and other discrete devices.
In one possible design, the system on a chip also includes memory to hold the necessary program instructions and data. The processor and the memory may be decoupled, provided on different devices, respectively, connected by wire or wirelessly, or the processor and the memory may be coupled on the same device.
Drawings
Fig. 1 is a schematic block diagram of a target tracking method according to the present invention.
FIG. 2 is a schematic block diagram of a step of determining whether there are coincident position points on a blank segment according to the present invention.
Fig. 3 is a schematic block diagram of a process flow for filling a blank segment according to the present invention.
Detailed Description
The technical scheme in the invention is further described in detail below with reference to the accompanying drawings.
The invention discloses a target tracking method based on a decentralised network, referring to fig. 1, the tracking method comprises the following steps:
S101, responding to the acquired tracking object, acquiring related information of the tracking object, drawing a position point group of the tracking object according to the related information, and marking the position point group as a first position point group;
S102, selecting a plurality of position points from a first position point group;
s103, obtaining suspected objects appearing at each position point;
S104, merging the suspected objects, drawing a position point group of the suspected objects according to the merging result, and marking the position point group as a second position point group; and
S105, calculating the coincidence degree of the first position point group and the second position point group, and screening the suspected objects according to the coincidence degree result to obtain the suspected objects associated with the tracked objects.
In general, in step S101, a tracking object is first determined or received, and then extracted and analyzed according to the deposition data of the tracking object in the database to obtain a set of position points of the suspected object associated with the tracking object, which is referred to herein as a first set of position points for description.
The sediment data of the tracking object in the database depends on the behavior data generated in the daily activities, such as connection of a mobile phone and a base station, the use condition of Bluetooth and data generated by various consumption. The data are stored in the database after being generated, or are requested by the database to the management end of the data when needed, and the management end sends the data to the database.
After the first location point set is generated, a plurality of location points are selected from the first location point set, that is, the content in step S102, where the location points are used to determine objects potentially closely contacted with the tracking object, and these objects are referred to herein as suspicious objects.
In step S103, a suspected object appearing at each location point is acquired, where the suspected object is directly associated with the location point. According to the foregoing, it can be known that the location points are generated based on the tracking object, and for a location point, there may be two dimensions of location and time, and the suspected object is screened by using the two dimensions of location and time, that is, the suspected object and the tracking object need to appear at the same location point, and meanwhile, the requirement of the time dimension needs to be met.
For example, taking a point in time or a period of time when the tracking object appears at a certain position point, the object appearing at the point in time or the period of time is included in the range of the suspected object.
In step S104, the suspected objects are merged, and the position point groups of the suspected objects are drawn according to the merging result and marked as second position point groups, wherein the second position point groups represent the coincidence degree of the position point groups of the suspected objects and the first position point groups.
For example, if a suspected object appears at a plurality of location points selected in step S102 and matches both the location and the time dimensions, then a set of location points, that is, a second set of location points, of the suspected object may be generated based on the location points.
In step S105, the degree of coincidence between the first position point set and the second position point set is calculated, and the suspected object is screened according to the result of the degree of coincidence, so as to obtain the suspected object associated with the tracked object. The specific overlap ratio calculating mode is as follows, M position points exist on the first position point group, N position points exist on the second position point group, and the overlap ratio q=n/M.
In addition, the calculation is required to be performed according to the time dimension, specifically, the occurrence time of the suspected object and the trace object at the same position point is determined.
At the same position point, the appearance time of the suspected object is T11, the appearance time of the tracking object is T12, and assuming that T1< T2, the weighted calculation amount P=T11/T12 at the position; of course, when T1> T2, the weight calculation amount p=1 at this position.
The coincidence ratio q= ((T11/T12) + (T21/T22) + … (TN 1/TN 2))/M, here, it is assumed that the occurrence times of the suspected objects at the same position point are smaller than the tracked object.
After the coincidence degree calculation is completed, comparing the coincidence degree with a set value or a set range, and when the coincidence degree obtained by calculation is larger than the set value or within the set range, determining that the suspected object is the suspected object related to the tracking object, otherwise, discarding the suspected object.
In the target tracking method based on the decentralization network, the association degree of the suspected object and the tracked object is judged through the position point group generated based on big data, and the relationship between the suspected object and the tracked object is converted into a mode of passing through the position point group (a first position point group and a second position point group) for judgment.
The invention uses the analysis and tracking mode of the decentralization network, judges the coincidence result of the first position point group and the second position point group through discrete position points, and gives the association degree of the suspected object and the tracked object. The analysis method of the centralized network is to integrate the information of the suspected object and the tracked object and then compare the integrated information, and calculate the integrated information by using an operation method such as weighting in the comparison process, but it is difficult to give a general processing method for weight distribution of each influencing factor.
Because the weights of the influencing factors are different for different individuals, it is difficult to use a set of more general influencing factor weight distribution modes to distribute the weights of the influencing factors, which leads to larger errors in the analysis process.
The method displays information related to movement of the suspected object and the tracked object on an airspace, and simultaneously uses the coincidence ratio to judge the coincidence part of the suspected object and the tracked object on the position point group. The weight assignment for each influencing factor mentioned in the foregoing may be automatically assigned by habit.
For a tracked object, the method can automatically find and track the potential suspected object through the position point group, and find the suspected object through analysis and judgment, so that the problem that the potential suspected object needs to be listed manually and has an oversized range and omission is avoided.
In some examples, the position points are selected multiple times on the first position point group, and there is incomplete coincidence between the position point in each selection process and the position point in the last selection process. The purpose of this approach is to be able to discover potential objects of interest as much as possible.
Since it is mentioned in the foregoing that the screening of the suspected object needs to calculate the overlap ratio between the first position point set and the second position point set, if the sample size of the first position point set is too large, more omission occurs in the overlap ratio calculation, and therefore, the position points need to be selected on the first position point set multiple times, so that the sample size of the first position point set can be reduced.
The incomplete coincidence of the position point in each selection process and the position point in the last selection process can enable different potential suspected objects to be aimed at in each processing process. In addition, if a certain potential object appears during multiple position point selections, it is apparent that the potential object is in close contact with the tracked object to a greater extent.
In summary, the method performs primary screening on the suspected objects according to the coincidence ratio of the first position point group and the second position point group, and performs secondary screening on the suspected objects according to the occurrence times of the suspected objects when the position points are selected for multiple times.
This has the advantage of enabling a more accurate localization of a suspected object because if a suspected object appears during multiple coincidence comparison, it is indicated that the suspected object has a high degree of intimate contact with the tracked object.
In some examples, calculating the overlap ratio of the first set of location points and the second set of location points includes the steps of:
s201, selecting a first position point group according to the time point of the starting position point and the time point of the stopping position point of the second position point group;
S202, counting the number of the same position points on the intercepted first position point group and the second position point group, and recording the number as a first number; and
S203, calculating the duty ratio of the first quantity in all the position points on the intercepted first position point group.
In a specific process, two position points, namely a start position point and a stop position point, are firstly determined, and then a second position point group is selected between the two position points. In this selection manner, the second position point group is arranged according to the time sequence, and then the selection is performed, and the position points in the first position point group are also required to be selected after being sequenced in the time dimension.
Then counting the number of the same position points on the intercepted first position point group and the second position point group, recording the number as a first number, and then calculating the duty ratio of the first number in all the position points on the intercepted first position point group.
By limiting the position points participating in analysis in the first position point group and the second position point group by using time, a more accurate judgment result can be obtained.
Of course, in the above manner, the number of the position points in the first position point group and the second position point group may be different, and the reason for this problem is that the big data is missing, please refer to fig. 2, and the following method is used to solve this problem:
S301, arranging and connecting position points in the first position point group and the second position point group on a time sequence, and performing deletion processing when connecting a default position point;
s302, calculating the area of a connecting line between a replacement connecting line of the missing processing region and a corresponding position point; and
S303, judging whether a coincident position point exists between the blank section and the corresponding first position point group according to the area and the blank section length.
The contents in steps S301 to S303 are that the blank space is filled first, then the area of the connection line between the replacement connection line of the missing processing area and the corresponding position point is calculated, and then whether the position point of the blank section and the corresponding first position point group is overlapped is judged according to the area and the length of the blank section.
The method can compensate the judgment problem of the omission of big data to a certain extent. Meanwhile, in order to further improve the accuracy in the determination, the following steps are introduced:
s401, calculating the distance between the vacant section and the corresponding first position point group; and
S402, discarding the part of the vacant section exceeding the allowable distance;
the method comprises the steps of dividing a blank section and a corresponding first position point group by using a plurality of parallel line segments.
The contents of step S401 and step S402 can obtain more accurate results in the area calculation process. Since the processing in step S401 and step S402 is performed, the length of the deletion processing is limited.
According to the above description, when the usage area and the length of the blank section are in negative correlation, that is, when the length of the blank section is longer, the usage area should be smaller, so that the higher the coincidence degree of the position point connecting lines in the first position point group and the second position point group can be described.
The length of the missing processing is limited, but the same calculation mode is still maintained in the area calculation process, which leads to more strict limitation conditions in the determination.
In addition, the method of filling the empty segment can be selected for processing, referring to fig. 3, and the specific steps are as follows:
S501, acquiring a starting position point and a cut-off position point of a vacant section;
S502, obtaining a plurality of paths according to the positions of the starting position point and the stopping position point on the map; and
S503, filling the blank section by using a path;
The path selects or selects a shortest path according to the suspected object.
Specifically, the calculation process required to be performed during the judgment is completed by means of virtually constructing the real moving track according to the simulation paths of the starting position point and the cut-off position point of the vacant section, and the shortest path is selected or selected according to the suspected object for the path selection.
The selection according to the suspected objects is based on the fact that the correlation content exists in the vacant section in the big data collected in the earlier stage, and if the correlation content does not exist, a shortest path is selected. This approach has the advantage of being more realistic and helping to get a more accurate decision.
The invention also provides a target tracking device, which comprises:
the first processing unit is used for responding to the acquired tracking object, acquiring the related information of the tracking object, drawing a position point group of the tracking object according to the related information and marking the position point group as a first position point group;
The first selecting unit is used for selecting a plurality of position points in the first position point group;
The second selecting unit is used for acquiring suspected objects appearing at each position point;
the second processing unit is used for merging the suspected objects, drawing a position point group of the suspected objects according to the merging result and marking the position point group as a second position point group; and
And the screening unit is used for calculating the coincidence ratio of the first position point group and the second position point group and screening the suspected objects according to the coincidence ratio result to obtain the suspected objects associated with the tracked objects.
Further, selecting the position points on the first position point group for multiple times, wherein the position points in each selection process are not completely overlapped with the position points in the last selection process.
Further, the suspected objects are screened for one time according to the coincidence degree of the first position point group and the second position point group;
And carrying out secondary screening on the suspected objects according to the occurrence times of the suspected objects when the position points are selected for multiple times.
Further, the method further comprises the following steps:
The intercepting unit is used for selecting the first position point group according to the time point of the starting position point and the time point of the stopping position point of the second position point group;
The statistics unit is used for counting the number of the same position points on the intercepted first position point group and the second position point group, and recording the number as a first number; and
A first calculation unit for a first number of duty cycles in all location points on the truncated first set of location points.
Further, the method further comprises the following steps:
The first filling unit is used for arranging and connecting the position points in the first position point group and the second position point group on a time sequence, and the default position point is subjected to deletion processing during connection;
the second calculation unit is used for calculating the area of the connecting line between the substitution connecting line of the missing processing area and the corresponding position point; and
And the judging unit is used for judging whether the overlapped position points exist between the vacant section and the corresponding first position point group according to the area and the length of the vacant section.
Further, the method further comprises the following steps:
the third calculation unit is used for calculating the distance between the vacant section and the corresponding first position point group; and
A discarding processing unit, configured to discard the portion of the empty segment exceeding the allowable distance;
the method comprises the steps of dividing a blank section and a corresponding first position point group by using a plurality of parallel line segments.
Further, the method further comprises the following steps:
The acquisition unit is used for acquiring the starting position point and the stopping position point of the vacant section;
the path planning unit is used for obtaining a plurality of paths according to the positions of the starting position point and the stopping position point on the map; and
The second filling unit is used for filling the vacant section by using one path;
The path selects or selects a shortest path according to the suspected object.
In one example, the unit in any of the above apparatuses may be one or more integrated circuits configured to implement the above methods, for example: one or more application specific integrated circuits (application specific integratedcircuit, ASIC), or one or more digital signal processors (DIGITAL SIGNAL processor, DSP), or one or more field programmable gate arrays (field programmable GATE ARRAY, FPGA), or a combination of at least two of these integrated circuit forms.
For another example, when the units in the apparatus may be implemented in the form of a scheduler of processing elements, the processing elements may be general-purpose processors, such as a central processing unit (central processing unit, CPU) or other processor that may invoke a program. For another example, the units may be integrated together and implemented in the form of a system-on-a-chip (SOC).
Various objects such as various messages/information/devices/network elements/systems/devices/actions/operations/processes/concepts may be named in the present invention, and it should be understood that these specific names do not constitute limitations on related objects, and that the named names may be changed according to the scenario, context, or usage habit, etc., and understanding of technical meaning of technical terms in the present invention should be mainly determined from functions and technical effects that are embodied/performed in the technical solution.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It should also be understood that in various embodiments of the present invention, first, second, etc. are merely intended to represent that multiple objects are different. For example, the first time window and the second time window are only intended to represent different time windows. Without any effect on the time window itself, the first, second, etc. mentioned above should not impose any limitation on the embodiments of the present invention.
It is also to be understood that in the various embodiments of the invention, where no special description or logic conflict exists, the terms and/or descriptions between the various embodiments are consistent and may reference each other, and features of the various embodiments may be combined to form new embodiments in accordance with their inherent logic relationships.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a computer-readable storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned computer-readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The present invention also provides a target tracking system, the system comprising:
one or more memories for storing instructions; and
One or more processors configured to invoke and execute the instructions from the memory to perform the method as described above.
The present invention also provides a computer program product comprising instructions which, when executed, cause the tracking system to perform operations of the tracking system corresponding to the above method.
The present invention also provides a chip system comprising a processor for implementing the functions involved in the above, e.g. generating, receiving, transmitting, or processing data and/or information involved in the above method.
The chip system can be composed of chips, and can also comprise chips and other discrete devices.
The processor referred to in any of the foregoing may be a CPU, microprocessor, ASIC, or integrated circuit that performs one or more of the procedures for controlling the transmission of feedback information described above.
In one possible design, the system on a chip also includes memory to hold the necessary program instructions and data. The processor and the memory may be decoupled, and disposed on different devices, respectively, and connected by wired or wireless means, so as to support the chip system to implement the various functions in the foregoing embodiments. Or the processor and the memory may be coupled to the same device.
Optionally, the computer instructions are stored in a memory.
Alternatively, the memory may be a storage unit in the chip, such as a register, a cache, etc., and the memory may also be a storage unit in the terminal located outside the chip, such as a ROM or other type of static storage device, a RAM, etc., that may store static information and instructions.
It will be appreciated that the memory in the present invention can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory.
The non-volatile memory may be a ROM, programmable ROM (PROM), erasable programmable ROM (erasablePROM, EPROM), electrically erasable programmable EPROM (EEPROM), or flash memory.
The volatile memory may be RAM, which acts as external cache. RAM is of a variety of different types, such as sram (STATIC RAM, SRAM), DRAM (DYNAMIC RAM, DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (doubledata RATE SDRAM, DDR SDRAM), enhanced SDRAM (ENHANCED SDRAM, ESDRAM), synchronous DRAM (SYNCH LINK DRAM, SLDRAM), and direct memory bus RAM.
The embodiments of the present invention are all preferred embodiments of the present invention, and are not intended to limit the scope of the present invention in this way, therefore: all equivalent changes in structure, shape and principle of the invention should be covered in the scope of protection of the invention.
Claims (2)
1. A method for tracking a target based on a decentralized network, comprising:
S101, responding to the acquired tracking object, acquiring related information of the tracking object, drawing a position point group of the tracking object according to the related information, and marking the position point group as a first position point group;
S102, selecting a plurality of position points from a first position point group;
s103, obtaining suspected objects appearing at each position point;
S104, merging the suspected objects, drawing a position point group of the suspected objects according to the merging result, and marking the position point group as a second position point group; and
S105, calculating the coincidence ratio of the first position point group and the second position point group, and screening the suspected objects according to the coincidence ratio result to obtain the suspected objects associated with the tracked objects;
calculating the contact ratio of the first position point group and the second position point group comprises:
s201, selecting a first position point group according to the time point of the starting position point and the time point of the stopping position point of the second position point group;
S202, counting the number of the same position points on the intercepted first position point group and the second position point group, and recording the number as a first number; and
S203, calculating the duty ratio of the first quantity in all the position points on the intercepted first position point group;
In the specific process, two position points, namely a starting position point and a stopping position point, are firstly determined, then a second position point group is selected between the two position points, in the selection mode, the second position point group is arranged according to a time sequence, then the selection is performed, and the position points in the first position point group are also required to be selected after being sequenced in a time dimension; counting the number of the same position points on the intercepted first position point group and the second position point group, recording the number as a first number, and then calculating the duty ratio of the first number in all the position points on the intercepted first position point group; the position points participating in analysis in the first position point group and the second position point group are limited by using time, so that a more accurate judgment result can be obtained;
When the number of position points in the first position point group and the second position point group is different, the method further comprises:
S301, arranging and connecting position points in the first position point group and the second position point group on a time sequence, and performing deletion processing when connecting a default position point;
s302, calculating the area of a connecting line between a replacement connecting line of the missing processing region and a corresponding position point; and
S303, judging whether a coincident position point exists between the blank section and the corresponding first position point group according to the area and the blank section length;
The contents in step S301 to step S303 are that firstly, filling up the vacant place, then calculating the area of the connecting line between the substitution connecting line of the missing processing area and the corresponding position point, and then judging whether the position point which is overlapped between the vacant section and the corresponding first position point group exists or not according to the area and the length of the vacant section; the method can make up for the judgment problem of the omission of big data to a certain extent;
selecting position points for multiple times on the first position point group, wherein the position points in each selection process are not completely overlapped with the position points in the last selection process;
Screening the suspected objects for one time according to the coincidence degree of the first position point group and the second position point group;
secondary screening is carried out on the suspected objects according to the occurrence times of the suspected objects when the position points are selected for multiple times;
Further comprises:
s401, calculating the distance between the vacant section and the corresponding first position point group; and
S402, discarding the part of the vacant section exceeding the allowable distance;
the method comprises the steps of dividing a blank section and a corresponding first position point group by using a plurality of parallel line segments;
The contents of steps S401 to S402 can obtain more accurate results in the area calculation process, and after the processing of steps S401 to S402, the length of the missing processing is limited; when the use area and the length of the vacant section are in negative correlation, namely when the length of the vacant section is longer, the use area is smaller, so that the higher the coincidence degree of the position point connecting lines in the first position point group and the second position point group can be described; the length of the missing processing is limited, but the same calculation mode is still maintained in the area calculation process, so that more strict limitation conditions are adopted in the judgment;
Filling the blank section is further included, and filling the blank section includes:
S501, acquiring a starting position point and a cut-off position point of a vacant section;
S502, obtaining a plurality of paths according to the positions of the starting position point and the stopping position point on the map; and
S503, filling the blank section by using a path;
The path selects or selects a shortest path according to the suspected object;
The method comprises the steps that a path is simulated according to a starting position point and a cut-off position point of a blank section, a calculation process needed to be carried out during judgment is completed in a mode of virtually constructing a real moving track, and a shortest path is selected or selected according to suspected objects for path selection; the basis of the selection according to the suspected objects is that the correlation content exists in the vacant section in the big data collected in the earlier stage, and if the content does not exist, a shortest path is selected so as to be more fit with the actual, and a more accurate judgment result is obtained.
2. A target tracking apparatus using the decentralised network-based target tracking method of claim 1, the apparatus comprising:
the first processing unit is used for responding to the acquired tracking object, acquiring the related information of the tracking object, drawing a position point group of the tracking object according to the related information and marking the position point group as a first position point group;
The first selecting unit is used for selecting a plurality of position points in the first position point group;
The second selecting unit is used for acquiring suspected objects appearing at each position point;
the second processing unit is used for merging the suspected objects, drawing a position point group of the suspected objects according to the merging result and marking the position point group as a second position point group; and
And the screening unit is used for calculating the coincidence ratio of the first position point group and the second position point group and screening the suspected objects according to the coincidence ratio result to obtain the suspected objects associated with the tracked objects.
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