CN115358621A - Crowd formation planning method and system based on characteristic anchor points - Google Patents

Crowd formation planning method and system based on characteristic anchor points Download PDF

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CN115358621A
CN115358621A CN202211073984.7A CN202211073984A CN115358621A CN 115358621 A CN115358621 A CN 115358621A CN 202211073984 A CN202211073984 A CN 202211073984A CN 115358621 A CN115358621 A CN 115358621A
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李弋豪
黄天羽
张骞
刘峻宇
杜梁楷
刘逸凡
唐明湘
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Beijing Institute of Technology BIT
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Abstract

The invention relates to a crowd formation planning method based on characteristic anchor points, which comprises the following steps: generating N frames of patterns corresponding to the crowd arrangement shape according to the time sequence; determining the point location distribution of the crowd corresponding to each frame of pattern; determining a mapping scheme from the ith frame to the (i + 1) th frame of pattern crowd point positions, specifically comprising selecting a characteristic anchor point set, determining a mapping relation of the anchor point set, defining a loss function of crowd formation transformation according to anchor points, and obtaining a macroscopic planning result with the lowest total loss; and obtaining the moving direction and speed of each person according to the mapping scheme. The invention provides a population formation planning method and system based on a characteristic anchor point, and provides an unbalanced population formation planning method and system with high controllability.

Description

Crowd formation planning method and system based on characteristic anchor points
Technical Field
The invention relates to a crowd formation planning method and system, in particular to a crowd formation planning method and system based on feature anchor points.
Background
In recent years, crowd formation transformation and planning have been developed in the fields of movie and television special effects, large-scale artistic performance (such as Olympic games on scenes), unmanned aerial vehicle formation performance and the like. Due to the introduction of space-time constraint of group motion, the group formation transformation has the characteristics of low motion loss, collision avoidance, shape adaptation and the like. Thus, crowd movement based on crowd formation transformation can often present a higher aesthetic visual perception than free crowd movement.
The basic idea of the existing research of formation transformation and planning of people groups is to specify a series of key processes, and people can be transformed into target shapes from initial shapes through smooth and collision-free/less motion transformation by a certain transformation method. A great deal of work is done to solve this process by bipartite matching optimization or constraint-based dynamic collaborative optimization methods. However, the existing methods have two major drawbacks: 1) The accessibility of the transformation process is ensured only by optimizing the overall consumption of the movement or keeping the pattern boundary, so that the integrity and continuity of people in the movement process can be kept between simple and similar pattern transformations, but the effect between the patterns with complex edges and low shape similarity is poor; 2) The group formation transformation process generated by the existing method is lack of flexibility, and the group formation transformation process with diversity cannot be generated for the given pattern constraint. This is a significant limitation to the user's design experience.
In fact, team behavior based on formation transformation has been widely applied to movie and television art creation and crowd performance. The existing related researches generate fixed group formation transformation results through a given pipeline, and the process is rigid and lacks creativity. For example, a method based on PLE criterion uses the overall motion loss of the group as a core optimization target for obtaining the formation transformation result, and actually, increasing a certain overall motion loss of the group may generate a more characteristic and aesthetic group motion. In fact, the evaluation index of the formation transformation of the group of people in the prior work is too single, the aesthetic feeling of the group movement is difficult to measure, and the generation of the result of diversity is a very effective means for obtaining the 'best' result.
Disclosure of Invention
The invention aims to provide a non-uniform and high-controllability crowd formation planning method and system aiming at the defects of the prior art, and the method and the system can generate high-diversity crowd motions based on established constraints, namely realize differentiated crowd formation transformation process planning.
The invention provides a crowd formation planning method based on characteristic anchor points, which comprises the following steps:
step one, generating N frames of patterns corresponding to the crowd arrangement shape according to a time sequence;
secondly, determining the point location distribution of the crowd corresponding to each frame of pattern;
step three, determining a mapping scheme from the ith frame to the (i + 1) th frame pattern crowd point location, wherein the specific method comprises the following steps:
3.1 selecting a set of feature anchors among the population F
Figure BDA0003830669580000027
The non-anchor set is A' = F-A;
3.2, determining the mapping relation of anchor point sets in the two frames of patterns;
3.3, defining a loss function of the crowd formation transformation according to the anchor point:
for the population F i Each non-anchor individual agent of ∈ A' i Then population F i Position p in i To the crowd F i+1 Middle position p i+1 Distance loss D of loss And azimuth loss O loss is fixed The meaning is:
Figure BDA0003830669580000021
Figure BDA0003830669580000022
wherein
Figure BDA0003830669580000023
a j ∈A i ，
Figure BDA0003830669580000024
a′ j ∈A i+1 Δ L = | | | L | - | | L' |; both omega and k are constant parameters;
defining a loss function:
V loss ＝εD loss +λO loss +μD
wherein epsilon, lambda and mu are proportional parameters, D is the measurement space distance from agt to agt' in the traditional method,
Figure BDA0003830669580000025
3.4, obtaining F i All agent pairs F in i+1 V of all positions loss， Obtaining a macroscopic planning result with the lowest total loss;
and step four, obtaining the movement direction and speed of each person according to the mapping scheme.
According to the population formation planning method provided by the embodiment of the invention, the method for selecting the characteristic anchor point set A in the step 3.1 comprises the following steps:
setting the number k of target anchor points to the crowd F i And F i+1 Dividing two groups of people into k subgroups respectively through k-means clustering, and then separating the distance from the gravity center of each subgroup
Figure BDA0003830669580000026
The nearest individual serves as an anchor point; center of gravity of subgroup
Figure BDA0003830669580000031
The components in each coordinate direction are:
Figure BDA0003830669580000032
X i is the position component in the X direction of any individual in the subgroup, Y i For the position component in the Y direction, and so on, W i Calculating weight parameters of different individuals when calculating the gravity center;
the method for determining the anchor point set mapping relation in the two-frame pattern in the step 3.2 comprises the following steps:
anchor point set A is obtained through calculation i And A i+1 And then, taking the negative value of the distance between the individual world coordinate points in the two sets as a weight to establish a complete bipartite graph, and obtaining the optimal matching result of the total weight as the mapping relation between the anchor points.
According to the population formation planning method provided by the embodiment of the invention, the method for selecting the characteristic anchor point set A and determining the mapping relation of the anchor point set in the steps 3.1 and 3.2 comprises the following steps:
acquiring the Edge of a crowd; for the crowd F i And F i+1 Edge of (2) i And Edge i+1 Respectively carrying out down-sampling to ensure Edge after sampling' i And Edge' i+1 The equipotential is satisfied:
Edge′ i ～Edge′ i+1
since crowd edges are continuous, edge' i And Edge' i+1 Can be regarded as two circular queues with equal length, and a certain agent is specified to map: edge' i (j)→Edge′ i+1 (k) And the other agents complete the mapping according to the sequence in the queue.
According to the population formation planning method provided by the embodiment of the invention, the optimal matching result of the total weight is obtained through a Kuhn-Munkres algorithm and is used as the mapping relation between anchor points.
The invention also provides a crowd formation planning system based on the characteristic anchor point, which comprises the following steps:
the pattern generating module is used for generating N frames of patterns corresponding to the crowd arrangement shape according to the time sequence;
the point location setting module is used for determining the point location distribution of the crowd corresponding to each frame of pattern;
the point location mapping module is used for determining a mapping scheme from the ith frame to the (i + 1) th frame pattern crowd point location;
the motion conversion module is used for obtaining the motion direction and the motion speed of each person according to the mapping scheme;
wherein, the point location mapping module includes:
anchor selection means for selecting a set of characteristic anchors among a population F
Figure BDA0003830669580000033
The non-anchor set is A' = F-A;
the anchor point mapping component is used for determining the mapping relation of the anchor point set in the two-frame pattern;
a loss function component for defining a loss function of the population formation transform according to the anchor point:
for the population F i Each non-anchor individual agent of ∈ A' i Then population F i Position p in i To the crowd F i+1 Middle position p i+1 Distance loss D of loss And azimuth loss O loss Is defined as follows:
Figure BDA0003830669580000041
Figure BDA0003830669580000042
wherein
Figure BDA0003830669580000043
a j ∈A i ，
Figure BDA0003830669580000044
a′ j ∈A i+1 Δ L = | | L | - | | L' |; both omega and k are constant parameters;
defining a loss function:
V loss ＝εD loss +λO loss +μD
wherein epsilon, lambda and mu are proportional parameters, D is the measurement space distance from agt to agt' in the traditional method,
Figure BDA0003830669580000045
population planning means for obtaining F i All agent pairs F i+1 V of all positions loss And obtaining a macroscopic planning result with the lowest total loss.
According to the population formation planning system provided by the embodiment of the invention, the method for selecting the characteristic anchor point set A by the anchor point selection component comprises the following steps:
setting the number k of target anchor points to a crowd F i And F i+1 Dividing two groups of people into k subgroups by k-means clustering respectively, and then separating the distance from the gravity center of each subgroup
Figure BDA0003830669580000046
The nearest individual serves as an anchor point; center of gravity of subgroup
Figure BDA0003830669580000047
The components in each coordinate direction are:
Figure BDA0003830669580000048
X i is the position component in the X direction of any individual in the subgroup, Y i For the position component in the Y direction, and so on, W i Calculating weight parameters of different individuals when calculating the gravity center;
the anchor mapping component calculates an anchor set A i And A i+1 And then, taking the negative value of the distance between the individual world coordinate points in the two sets as a weight to establish a complete bipartite graph, and obtaining the optimal matching result of the total weight as the mapping relation between anchor points.
According to the crowd formation planning system provided by the embodiment of the invention, the anchor point selection component acquires the Edge of the crowd and performs the crowd F i And F i+1 Edge of (2) i And Edge i+1 Respectively carrying out down-sampling to ensure Edge after sampling' i And Edge' i+1 The equipotential is satisfied:
Edge′ i ～Edge′ i+1
will Edge' i And Edge' i+1 As a population F i And F i+1 The set of anchor points of (a);
edge 'since the crowd Edge is continuous' i And Edge' i+1 Can be regarded as two circular queues with equal length, the anchor mapping component designates one of the agents to map: edge' i (j)→Edge′ i+1 (k) And the other agents complete mapping according to the sequence in the queue.
According to the crowd formation planning system provided by the embodiment of the invention, the anchor point mapping component obtains the optimal matching result of the total weight as the mapping relation between anchor points through a Kuhn-Munkres algorithm.
The invention further provides a terminal, which comprises a processor, an input device, an output device and a memory, wherein the memory is used for storing program codes, and the processor is configured to run the program codes and execute the people group formation planning method.
The present invention also provides a computer-readable storage medium having stored thereon program instructions which, when executed by a processor, cause the processor to perform the aforementioned method of crowd formation planning.
Advantageous effects
The invention provides a population formation planning method and system based on a characteristic anchor point, and provides an unbalanced population formation planning method and system with high controllability.
Drawings
FIG. 1 is a flow chart of a population formation planning method based on feature anchor points;
FIG. 2 is a schematic diagram of a point location distribution scheme obtained from a pattern;
FIG. 3 is a flowchart of a method for determining a people point location mapping scheme for an ith frame to an (i + 1) th frame pattern;
FIG. 4 is a schematic diagram of a matching map of an edge selection method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In order to obtain the effect of transforming the formation of the crowd with strong diversity, high boundary retentivity and low overall motion loss, embodiment 1 of the present invention provides a method for planning the formation of the crowd based on a feature anchor point, as shown in fig. 1, including the following steps:
generating N frames of patterns corresponding to the crowd arrangement shape according to a time sequence;
secondly, determining the point location distribution of the crowd corresponding to each frame of pattern; as shown in fig. 2, different point location distribution schemes can be obtained according to different population densities according to a pattern.
Step three, determining a mapping scheme from the ith frame to the (i + 1) th frame pattern crowd point, as shown in fig. 3, the specific method is as follows:
3.1 selecting a set of feature anchors among the population F
Figure BDA0003830669580000061
The non-anchor set is A' = F-A;
3.2, determining the mapping relation of anchor point sets in the two frames of patterns;
3.3, defining a loss function of the crowd formation transformation according to the anchor point:
after the crowd characteristic anchor point is selected and the mapping relation is determined, the whole crowd formation transformation can be calculated according to the anchor point. Since anchor points often account for only a very small portion (5% or even less) of the population as a whole, this process can be considered as a generalization of the small sample feature planning result to the population as a whole.
For the population F i Each non-anchor individual agent in (a) is belonged toA′ i Then the population F i Position p in i To the crowd F i+1 Middle position p i+1 Distance loss D of loss And the azimuth loss O loss Is defined as follows:
Figure BDA0003830669580000062
Figure BDA0003830669580000063
wherein
Figure BDA0003830669580000064
a j ∈A i ，
Figure BDA0003830669580000066
a′ j ∈A i+1 Δ L = | | L | - | | L' |; both omega and k are constant parameters;
defining a loss function:
V loss ＝εD loss +λO loss +μD
wherein epsilon, lambda and mu are proportional parameters, D is the measurement space distance from agt to agt' in the traditional method,
Figure BDA0003830669580000065
V loss can measure F i Any of the agents in anchor set A i Under the influence of (2) at F i+1 Any position in the table. V loss The lower agent has a greater tendency to move to that location in the formation transformation. V loss The influence of the anchor point is introduced on the basis of distance D measurement in the traditional method, so that the algorithm has a control degree far higher than that of the traditional method in the process of the formation transformation of the human team.
3.4 obtaining F i All agent pairs F i+1 V of all positions loss Obtaining a macroscopic planning result with the lowest total loss;
and step four, obtaining the movement direction and speed of each person according to the mapping scheme.
After the point location mapping scheme of every two adjacent frame patterns is obtained, the motion direction and speed of each person can be obtained according to the position of each point at different time, and therefore effective crowd formation planning is conducted.
The embodiment 2 of the invention provides a crowd formation planning method, and the method for selecting the characteristic anchor point set A in the step 3.1 comprises the following steps:
setting the number k of target anchor points to the crowd F i And F i+1 Dividing two groups of people into k subgroups by k-means clustering respectively, and then separating the distance from the gravity center of each subgroup
Figure BDA0003830669580000071
The nearest individual serves as an anchor point; center of gravity of subgroup
Figure BDA0003830669580000072
The components in each coordinate direction are:
Figure BDA0003830669580000073
X i is the position component in the X direction of any individual in the subgroup, Y i For the position component in the Y direction, and so on, W i Weight parameters for different individuals when calculating the center of gravity;
the method for determining the anchor point set mapping relationship in the two frame patterns in step 3.2 comprises the following steps:
anchor point set A is obtained through calculation i And A i+1 And then, taking the negative value of the distance between the individual world coordinate points in the two sets as a weight to establish a complete bipartite graph, and obtaining the optimal matching result of the total weight as the mapping relation between the anchor points.
The method provided in embodiment 2 is suitable for convex polygons with simple edges, or is used in combination with other methods as a method for selecting feature points in a group.
The embodiment 3 of the invention provides a population formation planning method different from that of the embodiment 2, and the method for selecting the characteristic anchor point set A and determining the mapping relation of the anchor point set in the steps 3.1 and 3.2 comprises the following steps:
acquiring the Edge of a crowd; for the crowd F i And F i+1 Edge of (2) i And Edge i+1 Respectively down-sampling to ensure Edge after sampling' i And Edge' i+1 The equipotential is satisfied:
Edge′ i ～Edge′ i+1
since crowd edges are continuous, edge' i And Edge' i+1 Can be regarded as two circular queues with equal length, and a certain agent is specified to map: edge' i (j)→Edge′ i+1 (k) And the other agents complete the mapping according to the sequence in the queue. The matching map of the edge selection method in example 3 is shown in fig. 4.
According to the crowd formation planning method in the embodiment 4 of the invention, the optimal matching result of the total weight is obtained through a Kuhn-Munkres algorithm and is used as the mapping relation between anchor points.
The invention provides a non-equilibrium and high-controllability group formation transformation method based on the characteristics of people. Through different crowd characteristic selection schemes, a crowd formation transformation process can be designed according to overall maintenance, edge maintenance or other different requirements. Compared with the existing scheme, the method can realize more accurate group motion control, and can generate various interesting results aiming at specific pattern design.
The invention also provides a crowd formation planning system based on the characteristic anchor point, which comprises the following steps:
the pattern generation module is used for generating N frames of patterns corresponding to the crowd arrangement shape according to the time sequence;
the point location setting module is used for determining the point location distribution of the crowd corresponding to each frame of pattern;
the point location mapping module is used for determining a mapping scheme from the ith frame to the (i + 1) th frame pattern crowd point location;
the motion conversion module is used for obtaining the motion direction and the motion speed of each person according to the mapping scheme;
wherein, the point location mapping module includes:
anchor selection means for selecting a set of characteristic anchors in the population F
Figure BDA0003830669580000081
The non-anchor set is A' = F-A;
the anchor point mapping component is used for determining the mapping relation of the anchor point set in the two frames of patterns;
a loss function component for defining a loss function of the population formation transform according to the anchor point:
for the population F i Each non-anchor individual agent of ∈ A' i Then population F i Position p in i To the crowd F i+1 Middle position p i+1 Distance loss D of loss And the azimuth loss O loss Is defined as:
Figure BDA0003830669580000082
Figure BDA0003830669580000083
wherein
Figure BDA0003830669580000084
a j ∈A i ，
Figure BDA0003830669580000085
a′ j ∈A i+1 Δ L = | | L | - | | L' |; both omega and k are constant parameters;
defining a loss function:
V loss ＝εD loss +λO loss +μD
wherein epsilon, lambda and mu are proportional parameters, D is the measurement space distance from agt to agt' in the traditional method,
Figure BDA0003830669580000086
population planning means for obtaining F i All agent pairs F i+1 V of all positions loss And obtaining a macroscopic planning result with the lowest total loss.
According to the crowd formation planning system provided by the embodiment of the invention, the method for selecting the characteristic anchor point set A by the anchor point selection component comprises the following steps:
setting the number k of target anchor points to a crowd F i And F i+1 Dividing two groups of people into k subgroups by k-means clustering respectively, and then separating the distance from the gravity center of each subgroup
Figure BDA0003830669580000091
The nearest individual serves as an anchor point; center of gravity of subgroup
Figure BDA0003830669580000092
The components in each coordinate direction are:
Figure BDA0003830669580000093
X i is the position component in the X direction of any individual in the subgroup, Y i For the position component in the Y direction, and so on, W i Weight parameters for different individuals when calculating the center of gravity;
the anchor mapping component calculates an anchor set A i And A i+1 And then, taking the negative value of the distance between the individual world coordinate points in the two sets as a weight to establish a complete bipartite graph, and obtaining the optimal matching result of the total weight as the mapping relation between the anchor points.
According to the crowd formation planning system provided by the embodiment of the invention, the anchor point selection component acquires the Edge of the crowd and performs the crowd F i And F i+1 Edge of (2) i And Edge i+1 Respectively carrying out down-sampling to ensure Edge after sampling' i And Edge' i+1 The equipotential is satisfied:
Edge′ i ～Edge′ i+1
will Edge' i And Edge' i+1 As a population F i And F i+1 The anchor point set of (2);
edge 'since the crowd Edge is continuous' i And Edge' i+1 Can be regarded as two circular queues with equal length, the anchor point mapping component designates a certain agent to map: edge' i (j)→Edge′ i+1 (k) And the other agents complete the mapping according to the sequence in the queue.
According to the crowd formation planning system provided by the embodiment of the invention, the anchor point mapping component obtains the optimal matching result of the total weight value through a Kuhn-Munkres algorithm to be used as the mapping relation between anchor points.
The invention also provides a terminal, which comprises a processor, an input device, an output device and a memory, wherein the memory is used for storing program codes, and the processor is configured to run the program codes and execute the crowd formation planning method.
The invention also provides a computer-readable storage medium having stored thereon program instructions which, when executed by a processor, cause the processor to carry out the aforementioned method of crowd formation planning.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art will be able to make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations fall within the scope of the invention defined by the appended claims.

Claims (10)

1. A crowd formation planning method based on feature anchor points is characterized by comprising the following steps:
generating N frames of patterns corresponding to the crowd arrangement shape according to a time sequence;
secondly, determining the point location distribution of the crowd corresponding to each frame of pattern;
step three, determining a mapping scheme from the ith frame to the (i + 1) th frame pattern crowd point position, wherein the specific method comprises the following steps:
3.1 selecting a set of feature anchors among the population F
Figure FDA0003830669570000011
The non-anchor set is A' = F-A;
3.2, determining the mapping relation of anchor point sets in the two frames of patterns;
3.3, defining a loss function of the crowd formation transformation according to the anchor point:
for the population F i Each non-anchor individual agent of ∈ A' i Then the population F i Position p in i To the crowd F i+1 Middle position p i+1 Distance loss D of loss And the azimuth loss O loss Is defined as follows:
Figure FDA0003830669570000012
Figure FDA0003830669570000013
wherein
Figure FDA0003830669570000014
Δ L = | | L | - | | L' |; both omega and k are constant parameters;
defining a loss function:
V loss ＝εD loss +λO loss +μD
wherein epsilon, lambda and mu are proportional parameters, D is the measurement space distance from agt to agt' in the traditional method,
Figure FDA0003830669570000015
3.4, obtaining F i All agent pairs F i+1 V of all positions loss Obtaining a macroscopic planning result with the lowest total loss;
and step four, obtaining the movement direction and speed of each person according to the mapping scheme.
2. The crowd formation planning method according to claim 1, wherein the method for selecting the feature anchor point set a in step 3.1 comprises:
setting the number k of target anchor points to the crowd F i And F i+1 Dividing two groups of people into k subgroups by k-means clustering respectively, and then separating the distance from the gravity center of each subgroup
Figure FDA0003830669570000016
The nearest individual serves as an anchor point; center of gravity of subgroup
Figure FDA0003830669570000017
The components in each coordinate direction are:
Figure FDA0003830669570000021
X i is the position component in the X direction of any individual in the subgroup, Y i For the position component in the Y direction, and so on, W i Calculating weight parameters of different individuals when calculating the gravity center;
the method for determining the anchor point set mapping relation in the two-frame pattern in the step 3.2 comprises the following steps:
anchor point set A is obtained through calculation i And A i+1 And then, taking the negative value of the distance between the individual world coordinate points in the two sets as a weight to establish a complete bipartite graph, and obtaining the optimal matching result of the total weight as the mapping relation between the anchor points.
3. The population formation planning method according to claim 1, wherein the method for selecting the characteristic anchor point set a and determining the mapping relationship of the anchor point set in step 3.1 and step 3.2 comprises:
acquiring the Edge of a crowd; for the crowd F i And F i+1 Edge of (2) i And Edge i+1 Respectively carrying out down-sampling to ensure Edge after sampling' i And Edge' i+1 The equipotential is satisfied:
Edge′ i ～Edge′ i+1
specifying one of the agent mappings: edge' i (j)→Edge′ i+1 (k) And the other agents complete mapping according to the sequence in the queue.
4. The crowd formation planning method based on claim 2, wherein the optimal matching result of the total weight is obtained through a Kuhn-Munkres algorithm and is used as a mapping relation between anchor points.
5. A crowd formation planning system based on feature anchor points is characterized by comprising:
the pattern generating module is used for generating N frames of patterns corresponding to the crowd arrangement shape according to the time sequence;
the point location setting module is used for determining the point location distribution of the crowd corresponding to each frame of pattern;
the point location mapping module is used for determining a mapping scheme from the ith frame to the (i + 1) th frame pattern crowd point location;
the motion conversion module is used for obtaining the motion direction and the motion speed of each person according to the mapping scheme;
wherein, the point location mapping module includes:
anchor selection means for selecting a set of characteristic anchors in the population F
Figure FDA0003830669570000022
The non-anchor set is A' = F-A;
the anchor point mapping component is used for determining the mapping relation of the anchor point set in the two frames of patterns;
a loss function component for defining a loss function of the population formation transform according to the anchor point:
for the population F i Each non-anchor individual agent of ∈ A' i Then the population F i Position p in i To the crowd F i+1 Middle position p i+1 Distance loss D of loss And the azimuth loss O loss Is defined as follows:
Figure FDA0003830669570000031
Figure FDA0003830669570000032
wherein
Figure FDA0003830669570000033
Δ L = | | | L | - | | L' |; both omega and k are constant parameters;
defining a loss function:
V loss ＝εD loss +λO loss +μD
wherein epsilon, lambda and mu are proportional parameters, D is the measurement space distance from agt to agt' in the traditional method,
Figure FDA0003830669570000034
crowd planning means for obtaining F i All agent pairs F in i+1 V of all positions loss And obtaining a macroscopic planning result with the lowest total loss.
6. The crowd formation planning system of claim 5, wherein the anchor selection component selects the feature anchor set A by:
setting the number k of target anchor points to the crowd F i And F i+1 Dividing two groups of people into k subgroups by k-means clustering respectively, and then separating the distance from the gravity center of each subgroup
Figure FDA0003830669570000035
The nearest individual serves as an anchor point; center of gravity of subgroup
Figure FDA0003830669570000036
The components in each coordinate direction are:
Figure FDA0003830669570000037
X i is the position component in the X direction of any individual in the subgroup, Y i For the position component in the Y direction, and so on, W i Calculating weight parameters of different individuals when calculating the gravity center;
the anchor point mapping component calculates to obtain an anchor point set A i And A i+1 And then, taking the negative value of the distance between the individual world coordinate points in the two sets as a weight to establish a complete bipartite graph, and obtaining the optimal matching result of the total weight as the mapping relation between anchor points.
7. The crowd formation planning system of claim 5, wherein the anchor point selection component obtains the Edge of the crowd, and for the crowd F i And F i+1 Edge of (2) i And Edge i+1 Respectively carrying out down-sampling to ensure Edge after sampling' i And Edge' i+1 The equipotential is satisfied:
Edge′ i ～Edge′ i+1
will Edge' i And Edge' i+1 As a population F i And F i+1 The set of anchor points of (a);
edge 'since the crowd Edge is continuous' i And Edge' i+1 Can be regarded as two circular queues with equal length, the anchor point mapping component designates a certain agent to map: edge' i (j)→Edge′ i+1 (k) And the other agents complete mapping according to the sequence in the queue.
8. The crowd formation planning system according to claim 6, wherein the anchor point mapping component obtains the optimal matching result of the total weight as the mapping relationship between anchor points through a Kuhn-Munkres algorithm.
9. A terminal comprising a processor, an input device, an output device and a memory, wherein the memory is configured to store program code, and the processor is configured to execute the program code to perform the human motion delta identification method according to any of claims 1-4.
10. A computer-readable storage medium, characterized in that the computer storage medium stores program instructions that, when executed by a processor, cause the processor to perform the human motion increment recognition method according to any one of claims 1 to 4.
CN202211073984.7A 2022-09-02 2022-09-02 Crowd formation planning method and system based on characteristic anchor points Pending CN115358621A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117372946A (en) * 2023-09-19 2024-01-09 日照市规划设计研究院集团有限公司 Tourist group tourist behavior identification method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117372946A (en) * 2023-09-19 2024-01-09 日照市规划设计研究院集团有限公司 Tourist group tourist behavior identification method
CN117372946B (en) * 2023-09-19 2024-04-16 日照市规划设计研究院集团有限公司 Tourist group tourist behavior identification method

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