CN110942019B - Analysis method for finding longest accompanying sub-path of two tracks - Google Patents
Analysis method for finding longest accompanying sub-path of two tracks Download PDFInfo
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- 238000000034 method Methods 0.000 claims abstract description 9
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- 230000009286 beneficial effect Effects 0.000 description 2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
Abstract
The invention discloses an analysis method for finding out the longest accompanying sub-path of two tracks, which comprises the following steps: optionally two tracks a and B and ordered in time; traversing the track points of the tracks A and B, and finding out track point pairs (P ', Q') with the smallest time difference in all track points; finding out all track point pairs (P ', Q') meeting the matching condition in the tracks A and B by adopting a recursion method to form an optimal matching result set; traversing the best matching result set to find out the longest accompanying sub-path meeting the requirement. The method effectively reduces the calculation complexity of the accompanying sub-paths, improves the calculation efficiency, is less influenced by the deviation value, and can be applied to the actual track data to rapidly analyze the accompanying sub-paths of a plurality of pairs of targets.
Description
Technical Field
The invention relates to the technical field of security and information, in particular to an analysis method for finding out the longest accompanying sub-paths of two tracks.
Background
At present, a plurality of monitoring devices in a security system can collect a large amount of behavior track data, the behavior track data comprise data such as a human face, MAC (media access control), IMSI (international mobile subscriber identity), IMEI (international mobile equipment identity) and the like, and different types of data are usually collected by different types of collecting devices, so that different types of track data are not directly related, but a lot of useful information can be analyzed from the track data through data mining, wherein one important information is a secondary path of two tracks, other suspicious persons which are often same as a certain target person can be found through the secondary paths of the two tracks, or the time and the place for collecting the two target persons can play an important role in the security field.
For the analysis of the accompanying sub-paths of the two tracks, the two possible tracks are separated after a period of time, and the two possible tracks return to the accompanying state after a period of time, namely the time difference and the distance of the two tracks are not specified and change frequently, and deviations sometimes occur, which are problems to be considered for the analysis of the accompanying sub-paths; in addition, the result is obtained by calculating a large amount of data in the accompanying sub-path analysis, and various track data analysis algorithms are available in the market, but most of the track data analysis algorithms are too complex and take long time, and cannot be effectively applied to the accompanying sub-path analysis.
Accordingly, the prior art has drawbacks and needs improvement.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an analysis method for finding the longest accompanying sub-path of two tracks.
The technical scheme of the invention is as follows: an analysis method for finding the longest accompanying sub-path of two tracks, comprising the steps of:
step S1, selecting two tracks A and B and sorting according to time;
step S2, traversing the track points of the tracks A and B, and finding out track point pairs (P ', Q') with the smallest time difference in all track points;
step S3, finding out all track point pairs (P ', Q') meeting the matching conditions in the tracks A and B by adopting a recursion method to form an optimal matching result set;
and S4, traversing the best matching result set, and finding out the longest accompanying sub-path meeting the requirements.
Further, the step S1 includes the steps of:
step S101, acquiring mass behavior track data which are acquired by acquisition equipment and change along with time through a terminal to construct a track database, and arbitrarily selecting two tracks A and B from the track database;
wherein, the terminal in step S101 includes a computer;
step S102, sorting the two tracks A and B according to time.
Further, the massive behavior trace obtained in step S101 includes one or more of face data, MAC data, IMSI data, and IMEI data.
Further, the step S2 includes the steps of:
step S201, traversing the track points P in the track A in time sequence, finding out the track point Q closest to the track points P in time from the track B, and calculating and recording the time difference of the track point pair (P, Q);
step S202 of finding out a trajectory point pair (P, Q) whose time difference is smallest and which satisfies a given maximum matching time difference of not more than the trajectory points P 'and Q' from among the trajectory point pairs (P, Q) in step S201, and composing the trajectory points P 'and Q' into a trajectory point pair (P ', Q').
Further, the time difference in step S201 is obtained by calculating a difference between the time corresponding to the trajectory point P and the time corresponding to the trajectory point Q.
Further, the step S3 includes the steps of:
step S301, dividing the track A and the track B into two sections by taking the time of the track point P 'and the track point Q' as separation points, wherein the track A is divided into a track A1 earlier than the track point P 'and a track A2 later than the track point P', and the track B is divided into a track B1 earlier than the track point Q 'and a track B2 later than the track point Q';
step S302, repeating the steps S102-S301 with the track A1 and the track B1 and the track A2 and the track B2 respectively until no matching track point pair (P ', Q') is found;
step S303, storing all the matched track point pairs (P ', Q') in the best matching result set.
Further, the step S4 includes the steps of:
step S401, sorting the track point pairs (P ', Q') in the best matching result set in step S303 in time sequence, and calculating the matching state of each track point pair (P ', Q') according to the distance and time difference between each track point pair (P ', Q') in the best matching result set;
step S402, selecting the first pair of track point pairs (P ', Q') with the matching state of 'matching' in the best matching result set as a starting point, and sequentially overlapping the next pair of track point pairs (P ', Q') with the matching state of 'matching' to form an accompanying sub-path C;
step S403, calculating the matching state of each track point pair (P ', Q') according to the distance and time difference between each track point pair (P ', Q') of each accompanying sub-path C, and counting the data value of the matching state;
step S404, calculating the accompanying coefficient of each accompanying sub-path C according to the formula of the data value of the matching state in step S403;
step S405, comparing the magnitude between the accompanying coefficient of each of the accompanying sub-paths C and the given minimum accompanying coefficient;
step S406, changing the starting point of the accompanying sub-path C with the accompanying coefficient smaller than the given minimum accompanying coefficient into the track point pair (P ', Q') with the next matching state being 'matching' as the starting point to form a new accompanying sub-path C;
step S407, repeating the steps S403 to S406 until the complete best matching result set is traversed;
in step S408, all the accompanying sub-paths C are saved in the sub-path set, and the accompanying sub-path C with the longest track and the largest satisfying accompanying coefficient is found from the sub-path set, i.e. the longest accompanying sub-path.
Further, the matching state in step S401 includes matching, deviation and uncertainty, and the matching result is obtained by comparing the "device acquisition range+time difference with the value of the distance" of each track point pair.
Further, the matching state in step S403 includes matching, deviation and uncertainty, and the matching result is obtained by comparing the "device acquisition range+time difference with the value of the distance" of each track point pair.
Further, the data value in the step S403 includes a matching value n1, a deviation value n2, and an uncertainty value n3, and the formula in the step S404 includes an accompanying parameter a=n2×1.0/(n1+n2), b=n1×1.0/(n1+n2+n3), and an accompanying coefficient=2×1/(1+exp (-5*a))/(1+exp (-8*b)) -1.
By adopting the scheme, the invention has the following beneficial effects:
the method effectively reduces the calculation complexity of the accompanying sub-paths, improves the calculation efficiency, is less influenced by the deviation value, and can be applied to the actual track data to rapidly analyze the accompanying sub-paths of a plurality of pairs of targets.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an analysis method for finding the longest accompanying sub-path of two tracks according to the present invention;
FIG. 2 is a flow chart of step S1 of the present invention;
FIG. 3 is a flow chart of step S2 of the present invention;
FIG. 4 is a flow chart of step S3 of the present invention;
fig. 5 is a flow chart of step S4 of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The invention will be described in detail below with reference to the drawings and the specific embodiments.
Referring to fig. 1, the present invention provides an analysis method for finding the longest accompanying sub-path of two tracks, comprising the steps of:
step S1, selecting two tracks A and B and sorting according to time;
step S2, traversing the track points of the tracks A and B, and finding out track point pairs (P ', Q') with the smallest time difference in all track points;
step S3, finding out all track point pairs (P ', Q') meeting the matching conditions in the tracks A and B by adopting a recursion method to form an optimal matching result set;
and S4, traversing the best matching result set, and finding out the longest accompanying sub-path meeting the requirements.
As a preferred embodiment, referring to fig. 2, the step S1 includes the following steps:
step S101, acquiring mass behavior track data which are acquired by acquisition equipment and change along with time through a terminal to construct a track database, and arbitrarily selecting two tracks A and B from the track database;
wherein, the terminal in step S101 includes a computer;
step S102, sorting the two tracks A and B according to time.
As a preferred embodiment, the massive behavior trace obtained in step S101 includes one or more of face data, MAC data, IMSI data, and IMEI data.
As a preferred embodiment, referring to fig. 3, the step S2 includes the following steps:
step S201, traversing the track points P in the track A in time sequence, finding out the track point Q closest to the track points P in time from the track B, and calculating and recording the time difference of the track point pair (P, Q);
step S202 of finding out a trajectory point pair (P, Q) whose time difference is smallest and which satisfies a given maximum matching time difference of not more than the trajectory points P 'and Q' from among the trajectory point pairs (P, Q) in step S201, and composing the trajectory points P 'and Q' into a trajectory point pair (P ', Q').
As a preferred embodiment, the time difference in step S201 is obtained by calculating the difference between the time corresponding to the trace point P and the time corresponding to the trace point Q.
As a preferred embodiment, the step S201 finds a pair of trajectory points (P i ，Q j ) Then, the next pair of track points is found out without re-head traversing the track B, and the track points P are directly compared with the track B i The first trace point at a late time starts the traversal, thereby allowing the computational complexity to be reduced from O (n 2 ) The method is reduced to O (n), so that the calculation complexity is effectively reduced, and the calculation efficiency is improved.
As a preferred embodiment, referring to fig. 4, the step S3 includes the following steps:
step S301, dividing the track A and the track B into two sections by taking the time of the track point P 'and the track point Q' as separation points, wherein the track A is divided into a track A1 earlier than the track point P 'and a track A2 later than the track point P', and the track B is divided into a track B1 earlier than the track point Q 'and a track B2 later than the track point Q';
step S302, repeating the steps S102-S301 with the track A1 and the track B1 and the track A2 and the track B2 respectively until no matching track point pair (P ', Q') is found;
step S303, storing all the matched track point pairs (P ', Q') in the best matching result set.
As a preferred embodiment, referring to fig. 5, the step S4 includes the following steps:
step S401, sorting the track point pairs (P ', Q') in the best matching result set in step S303 in time sequence, and calculating the matching state of each track point pair (P ', Q') according to the distance and time difference between each track point pair (P ', Q') in the best matching result set;
step S402, selecting the first pair of track point pairs (P ', Q') with the matching state of 'matching' in the best matching result set as a starting point, and sequentially overlapping the next pair of track point pairs (P ', Q') with the matching state of 'matching' to form an accompanying sub-path C;
step S403, calculating the matching state of each track point pair (P ', Q') according to the distance and time difference between each track point pair (P ', Q') of each accompanying sub-path C, and counting the data value of the matching state;
step S404, calculating the accompanying coefficient of each accompanying sub-path C according to the formula of the data value of the matching state in step S403;
step S405, comparing the magnitude between the accompanying coefficient of each of the accompanying sub-paths C and the given minimum accompanying coefficient;
step S406, changing the starting point of the accompanying sub-path C with the accompanying coefficient smaller than the given minimum accompanying coefficient into the track point pair (P ', Q') with the next matching state being 'matching' as the starting point to form a new accompanying sub-path C;
step S407, repeating the steps S403 to S406 until the complete best matching result set is traversed;
step S408, all the accompanying sub-paths C are stored in a sub-path set, and the accompanying sub-path C with the longest track and the largest satisfying accompanying coefficient is found out from the sub-path set, namely the longest accompanying sub-path;
note that, when there are a plurality of the accompanying sub-paths C having the longest track in the sub-path set, the accompanying sub-path C having the largest accompanying coefficient is taken as the longest accompanying sub-path.
As a preferred embodiment, the matching state in the step S401 includes matching, deviation and uncertainty, and the matching state of each track point pair (P ', Q') is obtained by comparing the "device acquisition range+time difference with the value of the distance; if the distance is smaller than the normal moving speed of the equipment acquisition range plus the time difference, the matching state of the track point pair is considered to be matching; if the distance is greater than the normal moving speed of the equipment acquisition range plus the time difference, the matching state of the track point pair is considered to be deviated; if the distance is equal to the 'equipment acquisition range + time difference' and the normal moving speed is equal to the distance, the matching state of the track point pair is considered to be uncertain.
As a preferred embodiment, the matching state in the step S403 includes matching, deviation and uncertainty, and the matching state of each track point pair (P ", Q") is obtained by comparing the "device acquisition range+time difference with the value of the distance; if the distance is smaller than the normal moving speed of the equipment acquisition range plus the time difference, the matching state of the track point pair is considered to be matching; if the distance is greater than the normal moving speed of the equipment acquisition range plus the time difference, the matching state of the track point pair is considered to be deviated; if the distance is equal to the 'equipment acquisition range + time difference' and the normal moving speed is equal to the distance, the matching state of the track point pair is considered to be uncertain.
As a preferred embodiment, the data values in the step S403 include a matching value n1, a deviation value n2 and an uncertainty value n3, and then the accompanying parameter a=n2×1.0/(n1+n2), b=n1×1.0/(n1+n2+n3), and the accompanying coefficient=2×1-1/(1+exp (-5*a))/(1+exp (-8*b)) -1, where the formula satisfies that the larger the matching value n1, the smaller the accompanying coefficient, the larger the deviation value n2 and the uncertainty value n3, and the larger the accompanying system.
Compared with the prior art, the invention has the following beneficial effects:
the method effectively reduces the calculation complexity of the accompanying sub-paths, improves the calculation efficiency, is less influenced by the deviation value, and can be applied to the actual track data to rapidly analyze the accompanying sub-paths of a plurality of pairs of targets.
The foregoing description of the preferred embodiment of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (3)
1. An analysis method for finding the longest accompanying sub-path of two tracks, comprising the steps of:
step S1, selecting two tracks A and B and sorting according to time;
step S2, traversing the track points of the tracks A and B, and finding out track point pairs (P ', Q') with the smallest time difference in all track points;
step S3, finding out all track point pairs (P ', Q') meeting the matching conditions in the tracks A and B by adopting a recursion method to form an optimal matching result set;
step S4, traversing the best matching result set, and finding out the longest accompanying sub-path meeting the requirements;
the step S1 includes the steps of:
step S101, acquiring mass behavior track data which are acquired by acquisition equipment and change along with time through a terminal to construct a track database, and arbitrarily selecting two tracks A and B from the track database;
wherein, the terminal in step S101 includes a computer;
step S102, sorting the two tracks A and B according to time;
the massive behavior tracks obtained in the step S101 comprise one or more of face data, MAC data, IMSI data and IMEI data;
the step S3 includes the steps of:
step S301, dividing the track A and the track B into two sections by taking the time of the track point P 'and the track point Q' as separation points, wherein the track A is divided into a track A1 earlier than the track point P 'and a track A2 later than the track point P', and the track B is divided into a track B1 earlier than the track point Q 'and a track B2 later than the track point Q';
step S302, repeating the steps S102-S301 with the track A1 and the track B1 and the track A2 and the track B2 respectively until no matching track point pair (P ', Q') is found;
step S303, storing all the matched track point pairs (P ', Q') in a best matching result set;
the step S4 includes the steps of:
step S401, sorting the track point pairs (P ', Q') in the best matching result set in step S303 in time sequence, and calculating the matching state of each track point pair (P ', Q') according to the distance and time difference between each track point pair (P ', Q') in the best matching result set;
step S402, selecting the first pair of track point pairs (P ', Q') with the matching state of 'matching' in the best matching result set as a starting point, and sequentially overlapping the next pair of track point pairs (P ', Q') with the matching state of 'matching' to form an accompanying sub-path C;
step S403, calculating the matching state of each track point pair (P ', Q') according to the distance and time difference between each track point pair (P ', Q') of each accompanying sub-path C, and counting the data value of the matching state;
step S404, calculating the accompanying coefficient of each accompanying sub-path C according to the formula of the data value of the matching state in step S403;
step S405, comparing the magnitude between the accompanying coefficient of each of the accompanying sub-paths C and the given minimum accompanying coefficient;
step S406, changing the starting point of the accompanying sub-path C with the accompanying coefficient smaller than the given minimum accompanying coefficient into the track point pair (P ', Q') with the next matching state being 'matching' as the starting point to form a new accompanying sub-path C;
step S407, repeating the steps S403 to S406 until the complete best matching result set is traversed;
step S408, all the accompanying sub-paths C are stored in a sub-path set, and the accompanying sub-path C with the longest track and the largest satisfying accompanying coefficient is found out from the sub-path set, namely the longest accompanying sub-path;
the matching state in step S401 includes matching, deviation and uncertainty, and the matching state of each track point pair obtains a matching result by comparing the value of the equipment acquisition range + time difference normal moving speed and the distance;
the matching state in step S403 includes matching, deviation and uncertainty, and the matching state of each track point pair obtains a matching result by comparing the value of the equipment acquisition range + time difference normal moving speed and the distance;
the data values in the step S403 include a matching value n1, a deviation value n2, and an uncertainty value n3, and the formula in the step S404 includes an accompanying parameter a=n2×1.0/(n1+n2), b=n1×1.0/(n1+n2+n3), and an accompanying coefficient=2×1-1/(1+exp (-5*a))/(1+exp (-8*b)) -1).
2. The method of analysis for finding the longest accompanying sub-path of two tracks according to claim 1, wherein said step S2 comprises the steps of:
step S201, traversing the track points P in the track A in time sequence, finding out the track point Q closest to the track points P in time from the track B, and calculating and recording the time difference of the track point pair (P, Q);
step S202 of finding out a trajectory point pair (P, Q) whose time difference is smallest and which satisfies a given maximum matching time difference of not more than the trajectory points P 'and Q' from among the trajectory point pairs (P, Q) in step S201, and composing the trajectory points P 'and Q' into a trajectory point pair (P ', Q').
3. The method according to claim 2, wherein the time difference in step S201 is obtained by calculating the difference between the time corresponding to the trace point P and the time corresponding to the trace point Q.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011015801A1 (en) * | 2009-08-06 | 2011-02-10 | Toshiba Research Europe Limited | Correlated probabilistic trajectories pedestrian motion detection using a decision forest |
| CN106650771A (en) * | 2016-09-29 | 2017-05-10 | 百度在线网络技术（北京）有限公司 | Cluster-analysis-based de-noising method and apparatus for trajectory |
| CN106960006A (en) * | 2017-02-24 | 2017-07-18 | 河海大学 | Measuring similarity system and its measure between a kind of different tracks |
| WO2017177965A1 (en) * | 2016-04-15 | 2017-10-19 | 深圳大学 | Track matching method based on time sequence |
| CN109947793A (en) * | 2019-03-20 | 2019-06-28 | 深圳市北斗智能科技有限公司 | Analysis method, device and the storage medium of accompanying relationship |
| CN109977109A (en) * | 2019-04-03 | 2019-07-05 | 深圳市甲易科技有限公司 | A kind of track data cleaning method and adjoint analysis method |
-
2019
- 2019-11-25 CN CN201911164357.2A patent/CN110942019B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011015801A1 (en) * | 2009-08-06 | 2011-02-10 | Toshiba Research Europe Limited | Correlated probabilistic trajectories pedestrian motion detection using a decision forest |
| WO2017177965A1 (en) * | 2016-04-15 | 2017-10-19 | 深圳大学 | Track matching method based on time sequence |
| CN106650771A (en) * | 2016-09-29 | 2017-05-10 | 百度在线网络技术（北京）有限公司 | Cluster-analysis-based de-noising method and apparatus for trajectory |
| CN106960006A (en) * | 2017-02-24 | 2017-07-18 | 河海大学 | Measuring similarity system and its measure between a kind of different tracks |
| CN109947793A (en) * | 2019-03-20 | 2019-06-28 | 深圳市北斗智能科技有限公司 | Analysis method, device and the storage medium of accompanying relationship |
| CN109977109A (en) * | 2019-04-03 | 2019-07-05 | 深圳市甲易科技有限公司 | A kind of track data cleaning method and adjoint analysis method |
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