CN106875417B - One kind is based on high-order figure across the associated multi-object tracking method of time domain - Google Patents
One kind is based on high-order figure across the associated multi-object tracking method of time domain Download PDFInfo
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Abstract
The testing result of each frame in video is obtained according to multi-target detection method first across the associated multi-object tracking method of time domain based on high-order figure the invention discloses one kind；Then the restricted function F (v on high-order side is responded and constructed by these detectionsi,vj) construct the common high-order figure across time domain；Later in order to include in the common high-order figure of rapidly extracting each time domain under local path section set, common high-order figure is first converted to random consistency high-order figure using the optimization method of RANSAC-style, further it is converted to common second order figure, subgraph search finally is carried out to ordinary second order figure, orbit segments multiple in each subgraph are connected according to the sequencing of time domain again, the long track of target is formed, to make the multiple target tracking in complex scene that there is good robustness.The present invention makes full use of the motion information of multiple target and presentation information in complex scene be associated with across time domain, solve adjacent objects it is apparently similar when occur identity exchange or local association mistake caused by track failure problem.
Description
Technical field
The invention belongs to field of intelligent video surveillance, in particular to it is a kind of based on high-order figure across the associated multiple target of time domain with
Track method.
Background technique
In recent years, attention of the multiple target tracking algorithm increasingly by scholar in computer vision field, multiple target tracking
Algorithm main purpose is to position and mark simultaneously the position of multiple mobile targets, is linked in sequence each marker bit according to time domain, into
And the long track of movement for obtaining multiple targets.In other words, target following technology is actually and is obtained using the video sequence of input
Detection information is obtained, and carries out certain association process to it, obtains the pursuit path of target.At present the multiple target video of mainstream with
Track technology may be summarized to be two parts: target detection and target following.Wherein, the main task of target detection part is from view
Interested target is detected in frequency image.Visual monitor and monitoring system generally use Still camera, from so-called static
Background in isolate moving object, realize the detection of target.This system generallys use building background model mode, utilizes threshold
Value calculates, and detects foreground target.Target following part main task is to will test the target of acquisition to be associated processing.It is based on
The multiple target tracking algorithm of Track association is the hot spot algorithm of domestic and foreign scholars' research.The cardinal principle of Track association algorithm be by
The multiple short and small tracking segments obtained after target detection carry out the association of many levels, final to obtain the continuous, smooth of target
Pursuit path.
Summary of the invention
It is an object of the invention to propose one kind based on high-order figure across the associated multi-object tracking method of time domain, first basis
The multi-target detection method of current main-stream obtains the testing result of each frame in video, then responds and construct high-order by these detections
Restricted function F (the v on sidei,vj) construct the common high-order figure across time domain；Later in order in the common high-order figure of rapidly extracting
Comprising high-order subgraph, common high-order figure is first converted to random consistency high-order figure using the optimization method of RANSAC-style,
It is further converted to common second order figure, subgraph search finally is carried out to ordinary second order figure, then by finally obtained each height
Multiple orbit segments in figure are connected according to the sequencing of time domain, form the long track of target, and this method makes in miscellaneous scene
Multiple target tracking have good robustness, efficiently solved adjacent objects it is apparently similar when occur identity exchange or office
The problems such as failure is tracked caused by portion's associated errors.
To achieve the goals above, the technical scheme is that having used a kind of associated more across time domain based on high-order figure
Method for tracking target includes the following steps:
Step A, long video sequence is divided into N equal portions, according to the offline inspection of multiple target in the time segments such as each
As a result the building of common high-order figure H is carried out respectively；
Step B, random consistency high-order figure is completed to commonly by gradually sampling and establishing multiple candidate connection samples
The approximation of high-order figure H；
Step C, the efficiency that element is searched to improve high-order subgraph carries out random consistency high-order figure and turns to ordinary second order figure H
It changes；
Step D, suitable weight measure function is selected to scan for the subgraph in ordinary second order figure；
Step E, the case where above subgraph searched does not meet physical limit is solved according to greedy algorithm, further according to part
The temporal order that orbit segment occurs is attached, to obtain the long track of target.
Further, specifically comprising steps are as follows in the step A:
Can be very time-consuming due to carrying out processing to a long video sequence series, it is unsatisfactory for the requirement of real-time, therefore is used
This long video sequence is divided into N equal portions by the method for parallel processing by different level, every equal portions include Ln frame, if last group of time
Section is then incorporated in previous group less than Ln frame.Then to each group of period respectively according to the restricted function F (v on high-order sidei,vj)∈
{ 0,1 } constructs across the when domain high order figure H=(V, E, α) of oneself, and wherein V represents the vertex (pursuit path section) in high-order figure, E generation
High-order side in table high-order figure, α represent the probability for belonging to vertex same high-order side.
Further, specifically comprising steps are as follows in the step B:
Step B-1 needs elder generation for all high-order subgraphs comprising target trajectory section in rapidly extracting high-order figure below
Common high-order figure is subjected to approximate processing, i.e., converts random consistency high-order figure for common high-order figure.First by gradually adopting
The method of sample obtains multiple candidate connection samples to establish random consistency high-order figure RCH={ S1,...,Si... }, wherein Si
For i-th of connection sample, each connection sample includes L vertex, this L vertex is from comprising ViSame high-order side in
It randomly selects, and meets restricted function F (vi,vj)=1, j=1 ..., L-1.
Step B-2, due to being that is randomly selected meet the vertex of restricted function, therefore there are insecure connection samples in B-1
This.In order to exclude above-mentioned bad connection sample, two kinds of confidence function C can be passed througha(s) and Cm(s) it takes into consideration to obtain
Sample is reliably connected, and then establishes the RCH comprising all important high-order subgraphs in former high-order figure H, wherein Ca(s) it is used to measure
The presentation similitude of target, Cm(s) it is used to the kinematic similarity of metric objective.
Further, specifically comprising steps are as follows in the step C:
It is completed using reliable random consistency high-order figure obtained in step B-2 to ordinary second order figure H'=(V', E', W')
Conversion, wherein W' is that two vertex belong to the probability of a line in second order figure.In order to determine in second order figure two vertex it
Between probability W', need to establish point set neighbour and scheme Ω=(ν, ε), in point set neighbour's figure, and if only if restricted function F
(vi,vjWhen)=1, there are a lines between both sides.Then RCH to ordinary second order figure H' is completed using Clique graph search algorithm
Conversion.
Further, specifically comprising steps are as follows in the step D:
Each vertex v in ordinary second order figure H' that step C is obtained firstpAs starting point, need to search forA top
Point, and according to the weight measure function Γ (v of definitionpUN(vp)), enable the point set of these point compositions to obtain maximum power
It is worth measure function value, the subgraph searched in this way hasA vertex.In order to avoid vertex number is degenerated in subgraph
Problem needs that a smallest dimension is arranged to the subgraph of searchAndIn addition, enabling yi=1 is
The indicator variable of subgraph vertex set U, works as yiWhen=1, vertex v is indicatediBelong to the subgraph of this search；Work as yi=0 is not belonging to
The subgraph.
Further, specifically comprising steps are as follows in the step E:
Due between the different subgraphs searched in step D there may be not meeting physical limit, such as it is same
Vertex (pursuit path section) can not belong to simultaneously two or more different targets, and this violates the physics facts.In order to eliminate
This conflict situations can be used greedy algorithm and be handled, and are first ranked up obtained subgraph according to fiducial probability size, obtain
Subgraphs sequence T after to sequence.It enables againFor the subgraph set of post-processing, according to greedy algorithm the vertex v of conflictiAdd
Enter to have in the subgraph of lap, i.e. viUT'→T'.Finally post-processing subgraph is connected each vertex according to temporal order
Get up to obtain the long track of target.
In general, through the invention it is contemplated above technical scheme is compared with the prior art, can obtain following has
Beneficial effect:
1, present invention employs step A and B, by a video signal process that time-consuming and capacity is big at N equal portions, with simultaneously
Capable mode replaces serially being handled, and greatly increases the processing speed of video, has reached the requirement of real-time；To it
In each time slice respectively construct a high-order figure, due to the side of high-order figure be by being constituted greater than 2 vertex, so
Multiple orbit segments in time slice can be subjected to across time domain association, make full use of the higher order relationship between orbit segment as association
Condition can greatly reduce the probability that mistake occurs in local association in this way, have good robustness to neighbouring apparent similar target；
2, present invention employs step C, step D and step E, the random consistency height obtained for rapidly extracting step B
High-order figure is further converted to ordinary second order figure, and is searched using Clique figure by the relevant high-order subgraph of institute for including in rank figure
Rope algorithm and weight measure function are handled, and can make hundred times of subgraph search improved efficiency or more in this way, finally by greedy algorithm
The case where not meeting physical limit can be effectively removed by handling obtained high-order subgraph set, guarantee the uniqueness and continuity of track
Property.
Detailed description of the invention
Fig. 1 be it is of the invention based on high-order figure across the associated multi-object tracking method flow chart of time domain.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with drawings and examples to this
Invention is described in further detail.
Implementation diagram of the invention is as shown in Figure 1, the following are a specific embodiment, and specific steps are successively are as follows:
Step A, long video sequence is divided into N equal portions, according to the offline inspection of multiple target in the time segments such as each
As a result the building of common high-order figure H is carried out respectively；
Can be very time-consuming due to carrying out processing to a long video sequence series, it is unsatisfactory for the requirement of real-time, therefore is used
This long video sequence is divided into N equal portions by the method for parallel processing by different level, every equal portions include Ln frame, if last group of time
Section is then incorporated in previous group less than Ln frame.Then to each group of period respectively according to the restricted function F (v on high-order sidei,vj)∈
{ 0,1 } constructs across the when domain high order figure H=(V, E, α) of oneself, and wherein V represents the vertex in high-order figure, and E is represented in high-order figure
High-order side, α represent the probability for belonging to vertex same high-order side.
Step B, it is arrived by gradually sampling and establishing multiple candidate connection samples to complete random consistency high-order figure (RCH)
The approximation of common high-order figure H；
For all high-order subgraphs comprising target trajectory section in rapidly extracting high-order figure below, needing first will be common high
Rank figure carries out approximate processing, i.e., converts random consistency high-order figure for common high-order figure.Method first by gradually sampling
Multiple candidate connection samples are obtained to establish random consistency high-order figure RCH={ S1,...,Si... }, wherein SiConnect for i-th
Sample is connect, each connection sample includes L vertex, this L vertex is from comprising ViSame high-order side in randomly select
, and meet restricted function F (vi,vj)=1, j=1 ..., L-1.
Due to it is above-mentioned be that is randomly selected meet the vertex of restricted function, therefore there are insecure connection samples.In order to arrange
Except above-mentioned bad connection sample, can pass through two kinds of confidence functions: one is the presentation similarity measurements of target:
Wherein χa(vi,vi+1) represent color histogram similarity, χw(vi,vi+1) represent texture similarity, χs(vi,vi+1)
Represent space similarity；Another kind is the kinematic similarity measurement of target:
Wherein dfp(vi,vi+1) and dbp(vi,vi+1) respectively represent forward prediction and back forecast.By two kinds of confidence function knots
Conjunction considers to obtain reliable connection sample, and then establishing includes the random consistent of all important high-order subgraphs in former high-order figure H
Property high-order figure RCH.
Step C, the efficiency that element is searched to improve high-order subgraph carries out random consistency high-order figure and turns to ordinary second order figure H
It changes；
It is completed using reliable random consistency high-order figure obtained in step B to ordinary second order figure H'='s (V', E', W')
Conversion, wherein W' is the probability that two vertex belong to a line in second order figure.In order to determine in second order figure between two vertex
Probability W', establish point set neighbour and scheme Ω=(ν, ε), wherein ν is and connect confidence level between sample S and be greater than threshold value λ's
Vertex set, ε are the restricted function F (v for whether meeting high-order side between two vertexi,vj)=1, i.e., in point set neighbour's figure,
And if only if restricted function F (vi,vjWhen)=1, there are a lines between both sides.Then it is completed using Clique graph search algorithm
Conversion of the RCH to ordinary second order figure H'.
Step D, suitable weight measure function is selected to scan for the subgraph in ordinary second order figure；
Each vertex v in ordinary second order figure H' that step C is obtained firstpAs starting point, need to search forA top
Point, and according to the weight measure function Γ (v of definitionpUN(vp)), enable the point set of these point compositions to obtain maximum power
It is worth measure function value, the subgraph searched in this way hasA vertex, particularly, in order to make vertex number relatively fewer
Subgraph can also smoothly search, can using set average side right weight values as weight measure function:
Wherein vertex set U=vpUN(vp).In order to avoid degenerate problem occurs in vertex number in subgraph, to the son of search
Figure one smallest dimension of settingAndIn addition, enabling y={ y1,...,yn}∈RnFor subgraph top
The indicator variable of point set U, works as yiWhen=1, vertex v is indicatediBelong to the subgraph of this search；Work as yi=0 is not belonging to the son
Figure.
Step E, the case where above subgraph searched does not meet physical limit is solved according to greedy algorithm, further according to part
The temporal order that orbit segment occurs is attached, to obtain the long track of target.
Due between the different subgraphs searched in step D there may be not meeting physical limit, such as it is same
Vertex (pursuit path section) can not belong to simultaneously two or more different targets, and this violates the physics facts.In order to eliminate
This conflict situations can be used greedy algorithm and be handled, and are first ranked up obtained subgraph according to fiducial probability size, obtain
Subgraphs sequence T={ T after to sequence1,...,Tn}.It enables againFor the subgraph set of post-processing, i-th is searched
SubgraphSo directly ΤiIt is put into set Τ ', otherwise according to greedy algorithm ΤiAddition has
In the subgraph of lap, i.e. TiUT'→Tj' in.Finally obtained post-processing subgraph is connected each vertex according to temporal order
It picks up to obtain the long track of target.
The above is not intended to limit the invention, all within the spirits and principles of the present invention, made any modification,
Equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (6)
1. one kind is based on high-order figure across the associated multi-object tracking method of time domain, which is characterized in that comprise the following steps that
Long video sequence is divided into N equal portions by step A, according to the offline inspection result of multiple target in the time segments such as each
The building of common high-order figure H is carried out respectively；
Step B completes random consistency high-order figure to common high-order by gradually sampling and establishing multiple candidate connection samples
Scheme the approximation of H；
Step C, the efficiency that element is searched to promote high-order subgraph carry out the conversion of random consistency high-order figure to ordinary second order figure H'；
Step D selects suitable weight measure function to scan for the subgraph in ordinary second order figure；
Step E solves the case where above subgraph searched does not meet physical limit, further according to local path according to greedy algorithm
The temporal order that section occurs is attached, to obtain the long track of target.
2. a kind of high-order figure that is based on according to claim 1 is across the associated multi-object tracking method of time domain, which is characterized in that
Include the following steps: in the step A
Can be very time-consuming due to carrying out processing to a long video sequence series, it is unsatisfactory for the requirement of real-time, therefore uses layering
This long video sequence is divided into N equal portions by the method for secondary parallel processing, every equal portions include Ln frame, if last group of period is not
Sufficient Ln frame, then be incorporated in previous group, then to each group of period respectively according to the restricted function F (v for constituting high-order figure sidei,vj)
∈ { 0,1 } constructs across when domain high order figure H=(V, E, α), wherein viIndicate the vertex of i-th of connection sample, vjIndicate j-th of company
The vertex of sample is connect, V represents the vertex in high-order figure, and E represents the high-order side in high-order figure, and it is high that α representative belongs to vertex same
The probability on rank side.
3. a kind of high-order figure that is based on according to claim 1 is across the associated multi-object tracking method of time domain, which is characterized in that
Include the following steps: in the step B
Step B-1 needs elder generation for all high-order subgraphs comprising target trajectory section in step C rapidly extracting high-order figure
Common high-order figure is subjected to approximate processing, i.e., converts random consistency high-order figure for common high-order figure, first by gradually adopting
The method of sample obtains multiple candidate connection samples to establish random consistency high-order figure RCH={ S1,...,Si... }, wherein Si
For i-th of connection sample, each connection sample includes L vertex, this L vertex is from comprising ViSame high-order figure side
In randomly select, and meet restricted function F (vi,vj)=1, j=1 ..., L-1, viIndicate the vertex of i-th of connection sample,
vjIndicate the vertex of j-th of connection sample；
Step B-2, what is randomly selected meet the vertex of restricted function due to being in B-1, therefore there are insecure connection sample, it is
Exclusion above-mentioned insecure connection sample, passes through two kinds of confidence function Ca(s) and Cm(s) it takes into consideration obtain can
The connection sample leaned on, and then establish the RCH comprising all important high-order subgraphs in former high-order figure H, wherein Ca(s) it is used to measure mesh
Target presentation similitude, Cm(s) it is used to the kinematic similarity of metric objective.
4. a kind of high-order figure that is based on according to claim 3 is across the associated multi-object tracking method of time domain, which is characterized in that
Include the following steps: in the step C
It is completed using reliable random consistency high-order figure RCH obtained in step B-2 to ordinary second order figure H'=(V', E', W')
Conversion, wherein W' is that two vertex belong to the probability of a line in second order figure, in order to determine in second order figure two vertex it
Between probability W', need to establish point set neighbour and scheme Ω=(ν, ε), in point set neighbour's figure, and if only if restricted function F
(vi,vjWhen)=1, there are a lines between both sides, then complete RCH to ordinary second order figure H' using Clique graph search algorithm
Conversion.
5. a kind of high-order figure that is based on according to claim 1 is across the associated multi-object tracking method of time domain, which is characterized in that
Include the following steps: in the step D
Each vertex v in ordinary second order figure H' that step C is obtained firstpAs starting point, need to search forA vertex, simultaneously
Define a weight measure function Γ (vp∪N(vp)), enable the point set of these point compositions to obtain maximum weight and estimates letter
Numerical value, the subgraph searched in this way haveA vertex, in order to avoid degenerate problem occurs in vertex number in subgraph, to searching
A smallest dimension is arranged in the subgraph of ropeAndIn addition, enabling y={ y1,...,yn}∈RnFor son
Figure summit set closes the indicator variable of U, works as yiWhen=1, vertex v is indicatediBelong to the subgraph of this search；Work as yi=0 is not belonging to this
Subgraph.
6. a kind of high-order figure that is based on according to claim 1 is across the associated multi-object tracking method of time domain, which is characterized in that
E includes the following steps: in the step
Since, there may be physical limit is not met, the same vertex can not between the different subgraphs searched in step D
Two or more different targets can be belonged to simultaneously, this violates the physics facts, in order to eliminate this conflict situations, can be used greedy
Greedy algorithm is handled, and is first ranked up obtained subgraph according to fiducial probability size, the subgraphs sequence T after being sorted,
It enables againFor the subgraph set of post-processing, according to greedy algorithm the vertex v of conflictiAddition has in the subgraph of lap,
That is viThen each vertex is connected post-processing subgraph according to temporal order to obtain the long track of target by ∪ T' → T'.
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Application publication date: 20170620 Assignee: Nanjing Nanyou Information Industry Technology Research Institute Co. Ltd. Assignor: Nanjing Post & Telecommunication Univ. Contract record no.: X2019980001257 Denomination of invention: Multi-target tracking method based on high-order graph cross-time domain association Granted publication date: 20191008 License type: Common License Record date: 20191224 |
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