CN106127803A - Human body motion capture data behavior dividing method and system - Google Patents

Abstract

The open a kind of human body motion capture data behavior dividing method of the present invention and system, the method includes: S1, using each frame data in human body motion capture data as each high dimensional data point, calculate the distance between each high dimensional data point and similarity, obtain distance matrix and similarity matrix；Human body motion capture data are processed by S2, local density and distance matrix according to each high dimensional data point, extract the behavior number in human body motion capture data and cluster centre；S3, according to similarity matrix, build undirected weighted graph；S4, human body motion capture data will be carried out behavior segmentation and be changed into undirected weighted graph is split, then be changed into spectral clustering, and use Spectral Clustering that human body motion capture data are clustered, obtain the behavior segmentation of human body motion capture data；S5, behavior segmentation is optimized, obtains the behavior cut-point of human body motion capture data.The present invention has preferable robustness, the suitability, effectiveness and accuracy.

Description

Human body motion capture data behavior dividing method and system

Technical field

The present invention relates to the process for human body motion capture data in computer animation.Have more particularly, to one Imitate unsupervised human body motion capture data behavior dividing method and system.

Background technology

With computer hardware technique and the development of computer graphics techniques, computer animation becomes a kind of important Digital Media form, the product that simultaneous computer animation combines as computer graphics and art, nowadays by extensively General it is applied to numerous fields, such as film and television special effect making, three-dimensional large-sized game, virtual reality etc..

In recent years, the human body motion capture technology sustainable development of optically-based equipment, sense of reality human body movement capturing data It is applied in computer animation.As it is shown in figure 1, human body motion capture data can preferably keep the verity of human motion With naturalness, but the cost that exercise data catches but is abnormal high, and capture-process is extremely complex, therefore transports for human body Dynamic reusing of data of seizure becomes necessary.Generally, human body motion capture Data Data amount is huge many, and behavior is enriched.Use people Work method, it is desirable to extract the single behavior campaign number required for people from the human body motion capture data base that data volume is huge According to, it is abnormal difficult and time-consuming.So, for human body motion capture data behavior split just become one indispensable Processing procedure.Meanwhile, human body motion capture data behavior segmentation has the biggest researching value and meaning.

Lot of domestic and international scholar studies for human body motion capture data behavior dividing method.Muller et al. (Muller et al.Efficient and robust annotation of motion capture data.In The proceedings of the 2009 ACM SIGGRAPH. page number: 17-26) use the pass extracting human body motion capture data Being feature, the method building relationship characteristic masterplate realizes the behavior segmentation of human body motion capture data.Lv et al. (Lv et al.Recognition and segmentation of 3-d human action using hmm and multi-class Adaboost.In Computer Vision-ECCV 2006. page number: 359-372) construct hidden Ma Erke by tranining database Husband's model, uses many sorting techniques to realize the behavior segmentation of human body motion capture data.But, this people based on supervised learning Body motor behavior dividing method, greatly can be limited by training dataset, can realize for the behavior comprised in data set Preferably behavior segmentation, and training data concentrates the behavior lacked to arise that segmentation errors.Barbic et al. (Barbic et al.Segmentation Motion Capture Data into Distinct Behaviors.Graphics Interface, 2004. page numbers: 185-194) think that different behaviors can use different intrinsic dimensionalities to represent, use main The method of component analysis carries out dimensionality reduction to human body motion capture data, then realizes human body by the analysis projection error on subspace The behavior segmentation of movement capturing data.But this method, has discarded the information beyond main constituent during dimensionality reduction, point Cut result and there is bigger error.On the basis of principal component analysis, Barbic et al. proposes row based on Probabilistic Principal Component Analysis For dividing method, this method uses makes noise the information beyond main constituent, builds probabilistic model, by calculate geneva away from From mode realize behavior segmentation.While the method realizes Data Dimensionality Reduction, process the information outside main constituent the most well, can To obtain preferable behavior dividing method, but the deficiency that the method exists is to cannot be distinguished by human body motion capture data sequence In, the identical behavior existed.

Accordingly, it is desirable to provide a kind of effective unsupervised human body motion capture data behavior dividing method and system.

Summary of the invention

It is an object of the invention to provide a kind of effective unsupervised human body motion capture data behavior dividing method and System.

For reaching above-mentioned purpose, the present invention uses following technical proposals:

A kind of human body motion capture data behavior dividing method, comprises the steps:

S1, using each frame data in human body motion capture data as each high dimensional data point, calculate each high dimensional data point it Between distance and similarity, obtain distance matrix and similarity matrix；

S2, according to the local density of each high dimensional data point and distance matrix, human body motion capture data are processed, extract people The behavior number comprised in body movement capturing data and cluster centre；

S3, according to similarity matrix, build undirected weighted graph；

S4, human body motion capture data will be carried out behavior segmentation and be changed into undirected weighted graph is split, then by right Undirected weighted graph carries out segmentation and is changed into spectral clustering, uses Spectral Clustering to cluster human body motion capture data, obtains The behavior segmentation of human body motion capture data；

S5, behavior segmentation to human body motion capture data are optimized, and obtain the row of human body motion capture data For cut-point.

Preferably, step S1 calculates the distance between each high dimensional data point and similarity, obtain distance matrix with similar The detailed process of property matrix is:

Calculate the distance between each high dimensional data point, the distance between each high dimensional data point constitute distance matrix, each height The computing formula of the distance between dimension strong point is as follows:

d_i,j=α d (p_i,p_j)+βd(v_i,v_j) i=1,2 ..., N；J=1,2 ..., N

Wherein, α and β is respectively weight, d_i,jFor representing high dimensional data point and the representative of the i-th frame human body motion capture data Distance between the high dimensional data point of jth frame human body motion capture data, N is the frame number of human body movement capturing data；d(v_i,v_j) w_kIt is the speed difference between the i-th frame human body motion capture data and jth frame human body motion capture data, d (v_i,v_j)=| v_i-v_j|, v_iIt it is the joint velocity in the i-th frame human body motion capture data；d(p_i,p_j)w_kIt is the i-th frame human body motion capture data and jth frame Posture between human body motion capture data is poor, following formula be calculated:

$d(p_i,p_j)=\left|\right|p_{i,0}-p_{j,0}|{|}^2+\underset{k=1}{\overset{m}{\Σ}}{w}_k|\left|log\left(q_{j,k}^{-1}{q}_{i,k}\right)\right|{|}^2$

Wherein, | | p_i,0-p_j,0||²w_kFor global translation distance；M is crucial joint number；w_kFor kth key joint institute The weight occupied；q_i,kIt it is the quaternary number representation in k crucial joint of the i-th frame human body motion capture data；

Calculate the similarity between each high dimensional data point, the similarity between each high dimensional data point constitute similarity square Battle array, the computing formula of the similarity between each high dimensional data point is as follows:

$w_{i,j}=e^{(-\frac{d_{i,j}^2}{2{\mathrm{\σ}}_i{\mathrm{\σ}}_j})}$

Wherein, w_i,jFor representing the high dimensional data point of the i-th frame human body motion capture data and representing jth frame human motion and catch Catch the similarity between the high dimensional data point of data；σ_iFor representing the phase of the high dimensional data point of the i-th frame human body motion capture data Like parameter, following formula it is calculated:

${\mathrm{\σ}}_i=\frac{1}{k^{\′}}\underset{m^{\′}=1}{\overset{k^{\′}}{\Σ}}{d}_{i,i+m^{\′}}$

Wherein, integer k ' the frame number that span is human body movement capturing data 1% to 10% between.

Preferably, the detailed process of step S2 is:

Calculate between local density and each high dimensional data point and the more highdensity high dimensional data point of each high dimensional data point Relative distance, computing formula is as follows:

${\mathrm{\ρ}}_i=\underset{j\∈I_s\backslash \left\{i\right\}}{\Σ}{e}^{-{\left(\frac{d_{i,j}}{d_c}\right)}^2}$

${\mathrm{\δ}}_i={\min }_{j:{\mathrm{\ρ}}_j>{\mathrm{\ρ}}_i}\left(d_{i,j}\right)$

Wherein, ρ_iFor representing the local density of the high dimensional data point of the i-th frame human body motion capture data；I_sFor each high dimension The high dimensional data point set of strong point composition；d_cFor clustering parameter, span is between 0.01 to 0.1；δ_iFor representing the i-th frame human body The high dimensional data point of movement capturing data and the relative distance of more high density data point；

To between local density and each high dimensional data point and the more highdensity high dimensional data point of each high dimensional data point Relative distance is normalized, and defines α_i'=ρ '_i*δ'_i；

The discrete derivative of the high dimensional data point that definition represents the i-th frame human body motion capture data is Δ_i=| α '_i-α'_i-θ|, θ For noise reduction parameters；

CalculateDiscrete derivative meansigma methodsAnd standard deviation ε_i, when meeting Δ_i＞ 3 ε_iTime think this α '_iValue meets Condition, the high dimensional data o'clock of the i-th corresponding frame human body motion capture data is as a cluster centre, thus obtains human motion Catch the cluster centre set { c included in data₁,c₂,c₃,...,c_K, number K of cluster centre is human motion and catches Catch the behavior number comprised in data.

Preferably, the detailed process of step S3 is:

Using each frame in N frame human body motion capture data as a node of undirected weighted graph, obtain the node of figure Set V={f₁,f₂,f₃,...,f_N, in figure, the limit weight between each node uses each internodal similarity to represent；

Calculating the degree of each node, by degree degree of the composition matrix of each node, the computing formula of the degree of each node is as follows:

$d_i=\underset{j=1}{\overset{N}{\Σ}}{w}_{i,j}$

Wherein, d_iFor undirected weighted graph represents the degree of the node of the i-th frame human body motion capture data.

Preferably, step S4 uses Spectral Clustering human body motion capture data are clustered, obtain human motion The detailed process of the behavior segmentation catching data is as follows:

Use t arest neighbors, similarity matrix is changed into sparse matrix S；

Calculate Laplacian MatrixWherein, E is unit matrix, and D is degree matrix；

Calculating K the minimal characteristic vector of Laplacian Matrix L, building minimal characteristic vector matrix is U ∈ R^N×K；

UseMode minimal characteristic vector matrix U is carried out Normalized, obtains matrix

Use k-means algorithm cluster matrixN row become K class, the human body fortune by timing recovery, after being clustered The dynamic behavior segmentation catching data.

Preferably, step S4 uses Spectral Clustering human body motion capture data are clustered, obtain human motion The detailed process of the behavior segmentation catching data is as follows:

Randomly select l data point, build matrix A ∈ R respectively^l×lWith B ∈ R^l×(N-l), A is for randomly selecting l high dimension Similarity matrix between strong point, B is between l the high dimensional data point randomly selected and residue (N-l) individual high dimensional data point Similarity matrix,；

Calculate matrix

Calculate matrix

Build matrixAnd it is carried out feature decomposition

CalculateK minimum characteristic vector for Laplacian Matrix L；

UseMode matrix U is normalized, To matrix

Use k-means algorithm cluster matrixN row become K class, the human body fortune by timing recovery, after being clustered The dynamic behavior segmentation catching data.

Preferably, the detailed process of step S5 is:

Energy function is used to be optimized the behavior segmentation of the human body motion capture data after cluster, described energy Flow function is

$F(S^{\′},G)=\underset{i^{\′},j^{\′}\∈1,2,...,K^{\′}}{\Σ}\underset{c_i=1}{\overset{K^{\′}}{\Σ}}{g}_{c_i}dist(s_{i^{\′}},s_{j^{\′}})$

Wherein, s_i'Represent be i-th in human body motion capture data ' individual behavior sequence, s_i'∈ S', S'={s₁,s₂, s₃,...,s_K'It it is the behavior segmentation set of human body motion capture number；Oriental matrixWhen Human body motion capture data f of the i-th frame_iWhen belonging to class cIt is otherwiseBe i-th ' individual behavior sequence Row and jth ' distance between individual behavior sequence,

Realize the Accurate Segmentation to human body motion capture data by the function defined based on dynamic programming principle, obtain people The behavior cut-point of body movement capturing data, described function based on dynamic programming principle definition is:

J (S ")=min{J (S "-1)+min{F (S "-1, G) }

Wherein, S " by the data sequence fragment being made up of human body motion capture data, J (S') is data sequence fragment S " Energy value.

A kind of human body motion capture data behavior segmenting system implementing said method, this system includes:

Distance matrix and similarity matrix build module, using each frame data in human body motion capture data as each higher-dimension Data point, calculates the distance between each high dimensional data point and similarity, obtains distance matrix and similarity matrix；

Human body motion capture data behavior number extraction module, according to local density and the distance matrix pair of each high dimensional data point Human body motion capture data process, and extract the behavior number and cluster centre comprised in human body motion capture data；

Undirected weighted graph builds module, according to similarity matrix, builds undirected weighted graph；

Human body motion capture data will be carried out behavior segmentation and be changed into undirected by human body motion capture data segmentation module Weighted graph is split, more undirected weighted graph will carry out segmentation is changed into spectral clustering, uses Spectral Clustering to human motion Catch data to cluster, obtain the behavior segmentation of human body motion capture data；

Optimize module, the behavior segmentation of human body motion capture data is optimized, obtains human body motion capture number According to behavior cut-point.

Beneficial effects of the present invention is as follows:

Technical scheme of the present invention is possible not only to effectively realize human body motion capture data behavior segmentation, and permissible The motion sequence fragment of the identical behavior in the human body motion capture data that distinguishing identifier is to be split.Technical scheme of the present invention There is preferable robustness, for human body motion capture data sequence behavior segmentation on the suitability, effectiveness and accuracy all There is certain superiority, it is possible to well meet actual demand.

Accompanying drawing explanation

Below in conjunction with the accompanying drawings the detailed description of the invention of the present invention is described in further detail.

Fig. 1 illustrates the schematic diagram that human body motion capture data behavior is split.

Fig. 2 illustrates the flow chart of the human body motion capture data behavior dividing method that embodiment 1 provides.

Fig. 3 illustrate two sections of human body motion capture data original in embodiment 1 and DTW distance solve after two sections of human bodies fortune Dynamic seizure Data Comparison figure, wherein, Fig. 3-a illustrates that original two section human body motion capture datagram, Fig. 3-b illustrate DTW distance Two sections of human body motion capture datagrams after solving.

Fig. 4 illustrate human body motion capture data behavior dividing method that embodiment 1 provides and existing methodical accuracy rate with Recall rate comparison diagram.

Fig. 5 illustrates the schematic diagram of the human body motion capture data behavior segmenting system that embodiment 2 provides.

Detailed description of the invention

In order to be illustrated more clearly that the present invention, below in conjunction with preferred embodiments and drawings, the present invention is done further Bright.Parts similar in accompanying drawing are indicated with identical reference.It will be appreciated by those skilled in the art that institute is concrete below The content described is illustrative and be not restrictive, and should not limit the scope of the invention with this.

As in figure 2 it is shown, the human body motion capture data behavior dividing method that the present embodiment provides comprises the steps:

S1, using each frame data in human body motion capture data as each high dimensional data point, calculate each high dimensional data point it Between distance and similarity, obtain distance matrix and similarity matrix；

Human body motion capture data are processed by S2, local density and distance matrix according to each high dimensional data point, carry Take the behavior number and cluster centre comprised in human body motion capture data, in this, as the priori of Spectral Clustering；

S3, according to similarity matrix, build undirected weighted graph；

S4, human body motion capture data will be carried out behavior segmentation and be changed into and undirected weighted graph is split, i.e. people Body movement capturing data behavior segmentation problem is changed into figure and cuts problem, more undirected weighted graph will carry out segmentation, and to be changed into spectrum poly- Class, i.e. cuts the relation between problem and spectral clustering by analysis chart, and NP (non-deterministic polynomial) is difficult The figure problem of cutting be changed into spectral clustering problem and solve, use Spectral Clustering that human body motion capture data are clustered, Obtain the behavior segmentation of human body motion capture data；

S5, behavior segmentation to human body motion capture data are optimized, and obtain the row of human body motion capture data For cut-point.

Wherein,

Step S1 is " using each frame in human body motion capture data as each high dimensional data point, each high dimensional data point composition height Dimension data point set I_s, calculate the distance between each high dimensional data point and similarity, obtain distance matrix and similarity matrix " tool Body process is as follows:

The human body motion capture data that the present embodiment uses are CMU (Carnegie Mellon University Ka Neiji Mei Long university) ASF/AMC file in data base.The human skeleton model of this document definition is 31 joints, has 62 certainly By spending, having 1 in 31 joints is root joint, and root joint has 6 degree of freedom.Human body motion capture data N frame, the i-th frame altogether Posture p of human body motion capture data_iFor removing other the articulate anglecs of rotation composition beyond root node, p_i={ a_i,1, a_i,2,a_i,3…a_i,56, i=1,2 ..., N, wherein, a_i,j'Be the jth in the i-th frame human body motion capture data ' individual degree of freedom, Represent an Euler angle, j'=1,2 ..., 56.Joint velocity v in i-th frame human body motion capture data_iBy adjacent pass Euclidean distance between joint posture is tried to achieve, such as formula (1):

$v_i=\left\{\begin{array}{cc}\sqrt{{(a_{i+1,1}-a_{i,1})}^2+{(a_{i+1,2}-a_{i,2})}^2+...+{(a_{i+1,56}-a_{i,56})}^2}& i\≠N\\ v_{i-1}& i=N\end{array}\right.---\left(1\right)$

Represent the high dimensional data point of the i-th frame human body motion capture data and represent the height of jth frame human body motion capture data Distance d between dimension strong point_i,jFormula (2) such as is used to calculate:

d_i,j=α d (p_i,p_j)+βd(v_i,v_j) i=1,2 ..., N；J=1,2 ..., N (2)

α and β is respectively weight, is disposed as 0.5 in the present embodiment, d (v_i,v_j) it is the i-th frame human body motion capture data And the speed difference between jth frame human body motion capture data, d (v_i,v_j)=| v_i-v_j|, d (p_i,p_j) represent the i-th frame human motion Catch the posture between data and jth frame human body motion capture data poor, posture difference d (p_i,p_j) it is calculated by formula (3):

$d(p_i,p_j)=\left|\right|p_{i,0}-p_{j,0}|{|}^2+\underset{k=1}{\overset{m}{\Σ}}{w}_k|\left|log\left(q_{j,k}^{-1}{q}_{i,k}\right)\right|{|}^2---\left(3\right)$

Wherein, | | p_i,0-p_j,0||²Represent is global translation distance；For posture gap, it is chosen at Behavioral change In play a decisive role m crucial joint (m=8 in the present embodiment)；w_kRepresent is the power occupied by kth key joint Weight, in the present embodiment, the value of weight occupied by each joint is as shown in table 1, and the occupied weight in remaining joint is set to 0, q_i,kIt it is the quaternary number representation in k crucial joint of the i-th frame human body motion capture data.For N frame human body motion capture number According to, after being calculated the distance between each high dimensional data point, the distance square of N*N can be made up of the distance between each high dimensional data point Battle array Dist, for the element d in Distance matrix D ist_i,j, d_i,j=d_j,i(i ≠ j) and d_i,j=0 (i=j).

Table 1: joint weighted value

Joint title	Weight wk
		Left and right hip joint	1.0000
Left and right knee joint	0.0901
		Left and right shoulder joint	0.7884
Left and right elbow joint	0.0247

Represent the high dimensional data point of the i-th frame human body motion capture data and represent the height of jth frame human body motion capture data Similarity w between dimension strong point_i,jFormula (4) such as is used to calculate:

$w_{i,j}=e^{(-\frac{d_{i,j}^2}{2{\mathrm{\σ}}_i{\mathrm{\σ}}_j})}---\left(4\right)$

In formula (4), represent the high dimensional data point of the i-th frame human body motion capture data and represent jth frame human body motion capture Distance d between the high dimensional data point of data_i,jIt is calculated by formula (2), represents the height of the i-th frame human body motion capture data Similar parameter σ at dimension strong point_iNeighbouring adaptive scale mode is used to calculate, computational methods such as formula (5):

${\mathrm{\σ}}_i=\frac{1}{k^{\′}}\underset{m^{\′}=1}{\overset{k^{\′}}{\Σ}}{d}_{i,i+m^{\′}}---\left(5\right)$

In formula (5), k' is the ε % of frame number contained by human body movement capturing data, i.e. k'=N ε % generally can choose taking of ε Value scope is between 1 to 10, i.e. the value of k' is N% to 10N%, ε=1 in this enforcement, i.e. k'=N%.

After being calculated the similarity between each high dimensional data point, the similarity between each high dimensional data point constitute N*N Similarity matrix W.

Step S2 " according to the local density of each high dimensional data point and distance matrix to human body motion capture data at Reason, extracts the behavior number and cluster centre comprised in human body motion capture data, and the priori in this, as Spectral Clustering is known Know " detailed process as follows:

The present embodiment uses a kind of unsupervised clustering method, and it can automatically extract clusters number, by experiment Understanding for human body motion capture data, the clustering method that the present embodiment uses is extracted cluster number and is this human body fortune The dynamic behavior number caught included in data.

Calculate between local density and each high dimensional data point and the more highdensity high dimensional data point of each high dimensional data point Relative distance, method is as follows:

${\mathrm{\ρ}}_i=\underset{j\∈I_s\backslash \left\{i\right\}}{\Σ}{e}^{-{\left(\frac{d_{i,j}}{d_c}\right)}^2}---\left(6\right)$

${\mathrm{\δ}}_i={\min }_{j:{\mathrm{\ρ}}_j>{\mathrm{\ρ}}_i}\left(d_{i,j}\right)---\left(7\right)$

Wherein, ρ_iFor representing the local density of the high dimensional data point of the i-th frame human body motion capture data；I_sFor each high dimension The high dimensional data point set of strong point composition；d_cFor clustering parameter, usual span is between 0.01 to 0.1, takes in the present embodiment Value is 0.05；δ_iThe relative distance of high dimensional data point Yu more high density data point for representing the i-th frame human body motion capture data； d_i,jFor representing the high dimensional data point of the i-th frame human body motion capture data and representing the high dimension of jth frame human body motion capture data Distance between strong point.

The condition meeting the high dimensional data point becoming cluster centre is: needs have bigger local density ρ simultaneously_iAnd Relative distance δ between bigger and more highdensity high dimensional data point_i.Owing to density p and distance δ are skimble-scamble, first It is normalized, defines α_i'=ρ '_i*δ'_i.Hopping behavior according to data seeks the α meeting condition_i' number, from And try to achieve cluster numbers, i.e. behavior number, method is as follows:

The discrete derivative of the high dimensional data point that definition represents the i-th frame human body motion capture data is Δ_i=| α '_i-α'_i-θ|, Noise reduction parameters θ be disposed to reduce error noise, calculateDiscrete derivative meansigma methodsAnd standard deviation ε_i, when full Foot Δ_i＞ 3 ε_iTime think this α '_iValue meets condition, and the high dimensional data o'clock of the i-th corresponding frame human body motion capture data is as one Individual cluster centre, thus obtain behavior number K and cluster centre set { c included in human body motion capture data₁,c₂, c₃,...,c_K}。

Step S3 " according to the similarity between movement capturing data frame, build undirected weighted graph and calculate the degree of each node " Detailed process as follows:

Using each frame in N frame human body motion capture data as a node of undirected weighted graph, obtain the node of figure Set V={f₁,f₂,f₃,...,f_N, in figure, the limit weight between each node uses each internodal similarity to represent, similarity Gaussian kernel form is used to try to achieve as shown in formula (4).Calculate the degree of each node, computational methods such as formula (8):

$d_i=\underset{j=1}{\overset{N}{\Σ}}{w}_{i,j}---\left(8\right)$

In formula (8), d_iFor undirected weighted graph represents the degree of the node of the i-th frame human body motion capture data.

After being calculated the degree of each node, by degree degree of the composition matrix D of each node.

Step S4 " will carry out behavior segmentation to be changed into and split undirected weighted graph, i.e. to human body motion capture data Human body motion capture data behavior segmentation problem is changed into figure and cuts problem, more undirected weighted graph will be carried out segmentation and be changed into spectrum Cluster, i.e. cuts the relation between problem and spectral clustering by analysis chart, NP (non-deterministic polynomial) The difficult figure problem of cutting is changed into spectral clustering problem and solves, and uses Spectral Clustering to gather human body motion capture data Class, obtains the behavior segmentation of human body motion capture data " in " human body motion capture data will be carried out behavior segmentation turn Become undirected weighted graph is split, i.e. human body motion capture data behavior segmentation problem be changed into figure and cut problem " tool Body process is as follows:

For figure cuts problem, the similarity within subgraph is big, and the similarity between different subgraph is less, this analogy In the behavior segmentation problem of human body motion capture data, for identical behavior, similarity is relatively big, and between different rows is Similarity is less.Therefore human body motion capture data behavior segmentation problem can be changed into figure and cut problem.

The sequence of given human body motion capture data, the Frame that each behavior is comprised is constituted in undirected weighted graph Subgraph F₁,F₂,...,F_K, as a example by there are two subgraphs, following relation exists for node between two subgraphs:

Similarity between two subgraphs is:

For this human body motion capture data sequence, segmentation to be realized, it is only necessary to make by human body motion capture number The figure segmentation Least-cost built according to sequence frame:

$cut(F_1,F_2,...,F_K)=\frac{1}{2}\underset{i=1}{\overset{K}{\Σ}}W(F_i,{\stackrel{\‾}{F}}_i)---\left(9\right)$

Thus can obtain, human body motion capture data behavior segmentation problem is converted into figure and cuts problem.

Step S4 " will carry out behavior segmentation to be changed into and split undirected weighted graph, i.e. to human body motion capture data Human body motion capture data behavior segmentation problem is changed into figure and cuts problem, more undirected weighted graph will be carried out segmentation and be changed into spectrum Cluster, i.e. cuts the relation between problem and spectral clustering by analysis chart, NP (non-deterministic polynomial) The difficult figure problem of cutting is changed into spectral clustering problem and solves, and uses Spectral Clustering to gather human body motion capture data Class, obtains the behavior segmentation of human body motion capture data " in " undirected weighted graph will carry out segmentation again, and to be changed into spectrum poly- Class, i.e. cuts the relation between problem and spectral clustering by analysis chart, and NP (non-deterministic polynomial) is difficult The figure problem of cutting be changed into spectral clustering problem and solve " detailed process as follows:

For figure cuts problem, two kinds of method for solving of Ratiocut and Ncut can be used, solve target and be respectively as follows:

$RatioCut(F_1,...,F_K)=\frac{1}{2}\underset{i=1}{\overset{K}{\Σ}}\frac{W(F_i,{\stackrel{\‾}{F}}_i)}{\left|F_i\right|}=\underset{i=1}{\overset{K}{\Σ}}\frac{cut(F_i,{\stackrel{\‾}{F}}_i)}{\left|F_i\right|}---\left(10\right)$

$Ncut(F_1,...,F_K)=\frac{1}{2}\underset{i=1}{\overset{K}{\Σ}}\frac{W(F_i,{\stackrel{\‾}{F}}_i)}{vol\left(F_i\right)}=\underset{i=1}{\overset{K}{\Σ}}\frac{cut(F_i,{\stackrel{\‾}{F}}_i)}{vol\left(F_i\right)}---\left(11\right)$

As a example by the sequence of aforementioned given human body motion capture data, two classification problems to be realized, use as above Two kinds of figures cut method for solving, solve target as follows:

$\underset{F\⋐V}{min}RatioCut(F,\stackrel{\‾}{F})---\left(12\right)$

$\underset{F\⋐V}{min}NCut(F,\stackrel{\‾}{F})---\left(13\right)$

That represent is subgraph F_cThe weights sum on middle limit, fixed for human body motion capture data sequence frame Justice instruction vector I=(I₁,I₂,I₃,...,I_N)'∈R^N, for I_iFor, definition

$I_i=\left\{\begin{array}{cc}\sqrt{vol\left(\stackrel{\‾}{F}\right)/vol\left(F\right)}& if{f}_i\∈F\\ -\sqrt{vol\left(F\right)/vol\left(\stackrel{\‾}{F}\right)}& if{f}_i\∈\stackrel{\‾}{F}\end{array}\right.---\left(14\right)$

For undirected weighted graph, build its Laplacian Matrix L, L=D-W (D is degree matrix, and W is similarity matrix).

L=D-W=D^-1/2(D-W)D^-1/2=E-D^-1/2WD^-1/2

$\begin{array}{c}{I}^{\′}LI=I^{\′}(D-W)I=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{d}_i{I}_i^2-\underset{i,j=1}{\overset{N}{\mathrm{\Σ}}}{I}_i{I}_J{w}_{ij}\\ =\frac{1}{2}(\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{d}_i{I}_i^2-2\underset{i,j=1}{\overset{N}{\mathrm{\Σ}}}{I}_i{I}_J{w}_{ij}+\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{d}_i{I}_i^2)=\frac{1}{2}\underset{i,j=1}{\overset{N}{\mathrm{\Σ}}}{w}_{ij}{(I_i-I_j)}^2\end{array}---\left(15\right)$

What E represented is unit matrix, by formula (15) can for the Laplacian Matrix of undirected weighted graph,

Wushu (14) substitutes into above formula and can obtain:

$\begin{array}{c}{I}^{\′}LI=\frac{1}{2}\underset{i,j=1}{\overset{N}{\mathrm{\Σ}}}{w}_{ij}{(I_i-I_j)}^2\\ =\frac{1}{2}(\underset{i\∈F,j\∈\stackrel{\‾}{F}}{\mathrm{\Σ}}{w}_{ij}{(\sqrt{\frac{vol\left(\stackrel{\‾}{F}\right)}{vol\left(F\right)}}+\sqrt{\frac{vol\left(F\right)}{vol\left(\stackrel{\‾}{F}\right)}})}^2+\underset{i\∈\stackrel{\‾}{F},j\∈F}{\mathrm{\Σ}}{w}_{ij}{(-\sqrt{\frac{vol\left(\stackrel{\‾}{F}\right)}{vol\left(F\right)}}-\sqrt{\frac{vol\left(F\right)}{vol\left(\stackrel{\‾}{F}\right)}})}^2)\\ =cut(F,\stackrel{\‾}{F})(\frac{vol\left(\stackrel{\‾}{F}\right)}{vol\left(F\right)}+\frac{vol\left(F\right)}{vol\left(\stackrel{\‾}{F}\right)}+2)\\ =cut(F,\stackrel{\‾}{F})(\frac{vol\left(F\right)+vol\left(\stackrel{\‾}{F}\right)}{vol\left(F\right)}+\frac{vol\left(F\right)+vol\left(\stackrel{\‾}{F}\right)}{vol\left(\stackrel{\‾}{F}\right)})\\ =vol\left(V\right)*NCut(F,\stackrel{\‾}{F})\end{array}---\left(16\right)$

For the N frame human body motion capture data that the present embodiment is used, vol (V) is constant value, then target letter Number is just equivalent to:

$\begin{array}{c}\underset{i=1}{\overset{N}{\Σ}}{I}_i=\underset{i\∈F}{\Σ}\sqrt{\frac{\left|vol\left(\stackrel{\‾}{F}\right)\right|}{\left|vol\left(F\right)\right|}}-\underset{i\∈\stackrel{\‾}{F}}{\Σ}\sqrt{\frac{\left|vol\left(F\right)\right|}{\left|vol\left(\stackrel{\‾}{F}\right)\right|}}\\ =\left|vol\left(F\right)\right|*\sqrt{\frac{\left|vol\left(\stackrel{\‾}{F}\right)\right|}{\left|vol\left(F\right)\right|}}-\left|vol\left(\stackrel{\‾}{F}\right)\right|*\sqrt{\frac{\left|vol\left(F\right)\right|}{\left|vol\left(\stackrel{\‾}{F}\right)\right|}}=0\end{array}---\left(17\right)$

$I^{\′}*1=\underset{i=1}{\overset{N}{\Σ}}{I}_i=0---\left(18\right)$

$\left|\right|I|{|}^2=\underset{i=1}{\overset{N}{\Σ}}{I}_i^2=|vol\left(F\right)|*\sqrt{\frac{\left|vol\left(\stackrel{\‾}{F}\right)\right|}{\left|vol\left(F\right)\right|}}+|vol\left(\stackrel{\‾}{F}\right)|*\sqrt{\frac{\left|vol\left(F\right)\right|}{\left|vol\left(\stackrel{\‾}{F}\right)\right|}}=vol\left(V\right)---\left(19\right)$

In sum, it is desirable to solution figure cuts the least cost function of problem and is equivalent to solve following problem:

$\begin{array}{c}\underset{I\∈R^N}{min}{I}^{\′}LI\\ subjectto{I}^{\′}*1=0,\left|\right|I\left|\right|=\sqrt{vol\left(V\right)}\end{array}---\left(20\right)$

Assuming LI=λ I, wherein λ and I is respectively eigenvalue and the characteristic vector of L, takes advantage of for LI=λ I the right and left simultaneously With I', I'LI=λ I'I (I'I=vol (V)) can be obtained, then minimize I'LI problem and be the eigenvalue λ problem minimizing it. So problem is converted to solve 2 eigenvalues of minimum of Laplacian Matrix L and characteristic vector problem.

Therefore, cut the relation between algorithm (Rationcut and Ncut) and spectral clustering by analyzing typical figure, send out The target that solves the most between the two is of equal value, therefore the figure that NP is difficult can be cut problem and use spectral clustering to solve.

Step S4 " will carry out behavior segmentation to be changed into and split undirected weighted graph, i.e. to human body motion capture data Human body motion capture data behavior segmentation problem is changed into figure and cuts problem, more undirected weighted graph will be carried out segmentation and be changed into spectrum Cluster, i.e. cuts the relation between problem and spectral clustering by analysis chart, NP (non-deterministic polynomial) The difficult figure problem of cutting is changed into spectral clustering problem and solves, and uses Spectral Clustering to gather human body motion capture data Class, obtains the behavior segmentation of human body motion capture data " in " use Spectral Clustering human body motion capture data are entered Row cluster, obtain the behavior segmentation of human body motion capture data " detailed process as follows:

The present embodiment is respectively adopted the algorithm of two kinds of spectral clusterings, processes N frame human body motion capture data.Two kinds of spectral clusterings Algorithm is respectively Spectral Clustering and the Spectral Clustering of application Nystrom algorithm of the sparse matrix of t arest neighbors.

(1) human body motion capture data are clustered by the Spectral Clustering using the sparse matrix of t arest neighbors, obtain people The detailed process of the behavior segmentation of body movement capturing data is as follows:

Input: N frame human body motion capture data sequence { f₁,f₂,f₃,...,f_N, each frame is seen as a high dimensional data Point, K is the behavior number that step S2 is tried to achieve, { c₁,c₂,c₃,...,c_KIt it is the cluster centre set tried to achieve of step S2；

1) similarity matrix W ∈ R is built by formula (4)^N×N；

2) using t arest neighbors, similarity matrix W is changed into sparse matrix S, and (generally, t may be defined as human body motion capture The 2% to 10% of data frame number, in the present embodiment, t is defined as the 2% of human body motion capture data frame number, thinks in the present embodiment Having limit between data point within the scope of this to be connected, between the data point within the scope of this, limit weight is not 0)；

3) Laplacian Matrix L is calculated,Wherein, E is unit matrix, and D is degree matrix, and S is sparse Matrix；

4) calculating K the minimal characteristic vector of Laplacian Matrix L, building minimal characteristic vector matrix is U ∈ R^N×K, its In to solve the method for its K minimal characteristic vector under conditions of known Laplacian Matrix L be mathematics side of the prior art Method；

5) useMode minimal characteristic vector matrix U is entered Row normalized, obtains matrix

6) k-means algorithm cluster matrix is usedN row become K class, by timing recovery, the human body after being clustered The behavior segmentation of movement capturing data.

(2) human body motion capture data are clustered by the Spectral Clustering using application Nystrom algorithm, obtain human body The behavior segmentation of movement capturing data " detailed process as follows:

Input: N frame human body motion capture data sequence { f₁,f₂,f₃,...,f_N, each frame is seen as a high dimensional data Point, K is that step S2 tries to achieve behavior number, { c₁,c₂,c₃,...,c_KIt is the cluster centre set tried to achieve of step S2, set l as defeated The 2% to 10% of the human body motion capture data frame number N entered, is specifically set as human body motion capture data frame number in the present embodiment 2%.

1) randomly select l data point, build matrix A ∈ R respectively^l×lWith B ∈ R^l×(N-l), A is for randomly selecting l higher-dimension Similarity matrix between data point, B is between l the high dimensional data point randomly selected and residue (N-l) individual high dimensional data point Similarity matrix；

2) matrix is calculated

3) matrix is calculated

4) matrix is builtAnd it is carried out feature decompositionV is matrix Characteristic vector, Σ be corresponding to characteristic vector V eigenvalue constitute diagonal matrix；

5) calculateK minimum characteristic vector for Laplacian Matrix L；

6) useMode matrix U is normalized place Reason, obtain matrix

7) k-means algorithm cluster matrix is usedN row become K class, by timing recovery, after i.e. can being clustered The behavior segmentation of human body motion capture data.

By obtaining above, by cluster mode, human motion is clustered into for N frame human body motion capture data and has caught Catch several behavior segmentation.

The behavior segmentation of human body motion capture data " is optimized, obtains human body fortune more accurately by step S5 The dynamic behavior cut-point catching data " detailed process as follows:

By step S4, for the human body motion capture data of input, the behavior segmentation of available human body motion capture number Set of segments S'={s₁,s₂,s₃,...,s_K', s_i'Represent be i-th in human body motion capture data ' individual behavior sequence.For Reduction behavior segmentation error, is defined as follows energy function:

$F(S^{\′},G)=\underset{i^{\′},j^{\′}\∈1,2,...,K^{\′}}{\Σ}\underset{c_i=1}{\overset{K^{\′}}{\Σ}}{g}_{c_i}dist(s_{i^{\′}},s_{j^{\′}})---\left(21\right)$

For human body motion capture data, oriental matrix G is defined asPeople when the i-th frame Body movement capturing data f_iWhen belonging to class cIt is otherwiseI.e. when human body motion capture data f of the i-th frame_iFor cluster During centerIt is otherwise

dist(s_i',s_j') distance between ' individual behavior sequence and jth ' the individual behavior sequence that is i-th, use DTW mode to enter Row calculates:

$DTW(s_{i^{\′}},s_{j^{\′}})=dist(s_{i^{\′}},s_{j^{\′}})=\arg \min \left\{\underset{f_{i^{\′}},\∈s_{i^{\′}},f_{j^{\′}}\∈s_{j^{\′}}}{\Σ}\right||f_{i^{\′}}-f_{j^{\′}}|\left|\right\}---\left(22\right)$

Fig. 3 illustrate original two section human body motion capture data and DTW distance solve after two sections of human body motion capture numbers According to comparison diagram, Fig. 3-a, Fig. 3-b show between two short sequences align distance solve example.

For the cluster result after timing recovery, i.e. the behavior segmentation of the human body motion capture data after cluster, The energy function be given using formula (21) is optimized process, and short sequence is grouped into the behavior sequence closest with it In, the function based on dynamic programming principle definition be given eventually through formula (23) realizes the essence to human body motion capture data Really segmentation:

J (S ")=min{J (S "-1)+min{F (S "-1, G) } (23)

In formula, " being defined as the data sequence fragment being made up of human body motion capture data, J (S') is defined as data to S Sequence fragment S " energy value, for each frame human body motion capture data, will they be divided into it represented by Among that behavior segment that behavior is consistent, therefore energy value needs to meet minimum.

The human body motion capture data behavior dividing method provided for the present embodiment, by great many of experiments, uses standard Error rate, recall rate framework show the effectiveness of our method.Result is as shown in Figure 4.

Embodiment 2

As it is shown in figure 5, the human body motion capture data behavior segmenting system that the present embodiment provides, including:

Distance matrix and similarity matrix build module, using each frame data in human body motion capture data as each higher-dimension Data point, calculates the distance between each high dimensional data point and similarity, obtains distance matrix and similarity matrix；

Human body motion capture data behavior number extraction module, according to local density and the distance matrix pair of each high dimensional data point Human body motion capture data process, and extract the behavior number and cluster centre comprised in human body motion capture data, with this Priori as Spectral Clustering；

Undirected weighted graph builds module, according to similarity matrix, builds undirected weighted graph；

Human body motion capture data will be carried out behavior segmentation and be changed into undirected by human body motion capture data segmentation module Weighted graph is split, and i.e. human body motion capture data behavior segmentation problem is changed into figure and cuts problem, then will be to undirected weighting Figure carries out segmentation and is changed into spectral clustering, i.e. cuts the relation between problem and spectral clustering by analysis chart, NP (non- Deterministic polynomial) the difficult figure problem of cutting is changed into spectral clustering problem and solves, and uses Spectral Clustering Human body motion capture data are clustered, obtains the behavior segmentation of human body motion capture data；

Optimize module, the behavior segmentation of human body motion capture data is optimized, obtains human body more accurately The behavior cut-point of movement capturing data.

Obviously, the above embodiment of the present invention is only for clearly demonstrating example of the present invention, and is not right The restriction of embodiments of the present invention, for those of ordinary skill in the field, the most also may be used To make other changes in different forms, cannot all of embodiment be given exhaustive here, every belong to this What bright technical scheme was extended out obviously changes or changes the row still in protection scope of the present invention.

Claims (8)

1. a human body motion capture data behavior dividing method, it is characterised in that the method comprises the steps:

S1, using each frame data in human body motion capture data as each high dimensional data point, calculate between each high dimensional data point Distance and similarity, obtain distance matrix and similarity matrix；

Human body motion capture data are processed by S2, local density and distance matrix according to each high dimensional data point, extract people The behavior number comprised in body movement capturing data and cluster centre；

S3, according to similarity matrix, build undirected weighted graph；

S4, human body motion capture data will be carried out behavior segmentation and be changed into undirected weighted graph is split, then will be to undirected Weighted graph carries out segmentation and is changed into spectral clustering, uses Spectral Clustering to cluster human body motion capture data, obtains human body The behavior segmentation of movement capturing data；

S5, behavior segmentation to human body motion capture data are optimized, and the behavior obtaining human body motion capture data divides Cutpoint.

Method the most according to claim 1, it is characterised in that step S1 calculates the distance between each high dimensional data point and Similarity, the detailed process obtaining distance matrix and similarity matrix is:

Calculate the distance between each high dimensional data point, the distance between each high dimensional data point constitute distance matrix, each high dimension The computing formula of the distance between strong point is as follows:

d_i,j=α d (p_i,p_j)+βd(v_i,v_j) i=1,2 ..., N；J=1,2 ..., N

Wherein, α and β is respectively weight, d_i,jFor representing the high dimensional data point of the i-th frame human body motion capture data and representing jth frame Distance between the high dimensional data point of human body motion capture data, N is the frame number of human body movement capturing data；d(v_i,v_j) it is i-th Speed difference between frame human body motion capture data and jth frame human body motion capture data, d (v_i,v_j)=| v_i-v_j|, v_iIt is i-th Joint velocity in frame human body motion capture data；d(p_i,p_j) it is the i-th frame human body motion capture data and jth frame human motion Catch the posture between data poor, following formula be calculated:

$d(p_i,p_j)=\left|\right|p_{i,0}-p_{j,0}|{|}^2+\underset{k=1}{\overset{m}{\Σ}}{w}_k|\left|log\left(q_{j,k}^{-1}{q}_{i,k}\right)\right|{|}^2$

Wherein, | | p_i,0-p_j,0||²For global translation distance；M is crucial joint number；w_kOccupied by kth key joint Weight；q_i,kIt it is the quaternary number representation in k crucial joint of the i-th frame human body motion capture data；

Calculate the similarity between each high dimensional data point, the similarity between each high dimensional data point constitute similarity matrix, respectively The computing formula of the similarity between high dimensional data point is as follows:

$w_{i,j}=e^{(-\frac{d_{i,j}^2}{2{\mathrm{\σ}}_i{\mathrm{\σ}}_j})}$

Wherein, w_i,jFor representing the high dimensional data point of the i-th frame human body motion capture data and representing jth frame human body motion capture number According to high dimensional data point between similarity；σ_iFor representing the similar ginseng of the high dimensional data point of the i-th frame human body motion capture data Number, is calculated by following formula:

${\mathrm{\σ}}_i=\frac{1}{k^{\′}}\underset{m^{\′}=1}{\overset{k^{\′}}{\Σ}}{d}_{i,i+m^{\′}}$

Wherein, integer k ' the frame number that span is human body movement capturing data 1% to 10% between.

Method the most according to claim 2, it is characterised in that the detailed process of step S2 is:

Calculate the phase between local density and each high dimensional data point with the more highdensity high dimensional data point of each high dimensional data point Adjusting the distance, computing formula is as follows:

${\mathrm{\ρ}}_i=\underset{j\∈I_s\backslash \left\{i\right\}}{\Σ}{e}^{-{\left(\frac{d_{i,j}}{d_c}\right)}^2}$

${\mathrm{\δ}}_i={\min }_{j:{\mathrm{\ρ}}_j>{\mathrm{\ρ}}_i}\left(d_{i,j}\right)$

Wherein, ρ_iFor representing the local density of the high dimensional data point of the i-th frame human body motion capture data；I_sFor each high dimensional data point The high dimensional data point set of composition；d_cFor clustering parameter, span is between 0.01 to 0.1；δ_iFor representing the i-th frame human motion Catch the high dimensional data point of data and the relative distance of more high density data point；

Relative between local density and each high dimensional data point with the more highdensity high dimensional data point of each high dimensional data point Distance is normalized, and defines α_i'=ρ_i'*δ_i'；

The discrete derivative of the high dimensional data point that definition represents the i-th frame human body motion capture data is Δ_i=| α '_i-α'_i-θ|, θ is fall Make an uproar parameter；

CalculateDiscrete derivative meansigma methodsAnd standard deviation ε_i, when meeting Δ_i＞ 3 ε_iTime think this α '_iValue meets bar Part, the high dimensional data o'clock of the i-th corresponding frame human body motion capture data is as a cluster centre, thus obtains human motion and catch Catch the cluster centre set { c included in data₁,c₂,c₃,...,c_K, number K of cluster centre is human body motion capture The behavior number comprised in data.

Method the most according to claim 3, it is characterised in that the detailed process of step S3 is:

Using each frame in N frame human body motion capture data as a node of undirected weighted graph, obtain node set V of figure ={ f₁,f₂,f₃,...,f_N, in figure, the limit weight between each node uses each internodal similarity to represent；

Calculating the degree of each node, by degree degree of the composition matrix of each node, the computing formula of the degree of each node is as follows:

$d_i=\underset{j=1}{\overset{N}{\Σ}}{w}_{i,j}$

Wherein, d_iFor undirected weighted graph represents the degree of the node of the i-th frame human body motion capture data.

Method the most according to claim 4, it is characterised in that use Spectral Clustering to human body motion capture in step S4 Data cluster, and the detailed process of the behavior segmentation obtaining human body motion capture data is as follows:

Use t arest neighbors, similarity matrix is changed into sparse matrix S；

Calculate Laplacian MatrixWherein, E is unit matrix, and D is degree matrix；

Calculating K the minimal characteristic vector of Laplacian Matrix L, building minimal characteristic vector matrix is U ∈ R^N×K；

UseMode minimal characteristic vector matrix U is normalized Process, obtain matrix

Use k-means algorithm cluster matrixN row become K class, by timing recovery, the human motion after being clustered is caught Catch the behavior segmentation of data.

Method the most according to claim 4, it is characterised in that use Spectral Clustering to human body motion capture in step S4 Data cluster, and the detailed process of the behavior segmentation obtaining human body motion capture data is as follows:

Randomly select l data point, build matrix A ∈ R respectively^l×lWith B ∈ R^l×(N-l), A is for randomly selecting l high dimensional data point Between similarity matrix, B is similar between l high dimensional data point randomly selecting and residue (N-l) individual high dimensional data point Property matrix,；

Calculate matrix

Calculate matrix

Build matrixAnd it is carried out feature decomposition

CalculateK minimum characteristic vector for Laplacian Matrix L；

UseMode matrix U is normalized, obtain square Battle array

Use k-means algorithm cluster matrixN row become K class, by timing recovery, the human motion after being clustered is caught Catch the behavior segmentation of data.

7. according to the method described in claim 5 or 6, it is characterised in that the detailed process of step S5 is:

Energy function is used to be optimized the behavior segmentation of the human body motion capture data after cluster, described energy letter Number is

$F(S^{\′},G)=\underset{i^{\′},j^{\′}\∈1,2,...,K^{\′}}{\Σ}\underset{c_i=1}{\overset{K^{\′}}{\Σ}}{g}_{c_i}dist(s_{i^{\′}},s_{j^{\′}})$

Wherein, s_i'Represent be i-th in human body motion capture data ' individual behavior sequence, s_i'∈ S', S'={s₁,s₂, s₃,...,s_K'It it is the behavior segmentation set of human body motion capture number；Oriental matrixWhen Human body motion capture data f of the i-th frame_iWhen belonging to class cIt is otherwisedist(s_i',s_j') be i-th ' individual behavior sequence Row and jth ' distance between individual behavior sequence,

Realize the Accurate Segmentation to human body motion capture data by the function defined based on dynamic programming principle, obtain human body fortune The dynamic behavior cut-point catching data, described function based on dynamic programming principle definition is:

J (S ")=min{J (S "-1)+min{F (S "-1, G) }

Wherein, S " by the data sequence fragment being made up of human body motion capture data, J (S') is data sequence fragment S " energy Value.

8. the human body motion capture data behavior segmenting system implementing method as claimed in claim 1, it is characterised in that should System includes:

Distance matrix and similarity matrix build module, using each frame data in human body motion capture data as each high dimensional data Point, calculates the distance between each high dimensional data point and similarity, obtains distance matrix and similarity matrix；

Human body motion capture data behavior number extraction module, local density and distance matrix according to each high dimensional data point are to human body Movement capturing data processes, and extracts the behavior number and cluster centre comprised in human body motion capture data；

Undirected weighted graph builds module, according to similarity matrix, builds undirected weighted graph；

Human body motion capture data will be carried out behavior segmentation and be changed into undirected weighting by human body motion capture data segmentation module Figure is split, more undirected weighted graph will carry out segmentation is changed into spectral clustering, uses Spectral Clustering to human body motion capture Data cluster, and obtain the behavior segmentation of human body motion capture data；

Optimize module, the behavior segmentation of human body motion capture data is optimized, obtains human body motion capture data Behavior cut-point.