Moving object track clustering method based on multi-dimensional distance measurement

A technology of distance measurement and trajectory clustering, which is applied in the field of trajectory clustering, can solve problems such as redundancy, inaccurate data, and low quality of trajectory clustering, and achieve the effects of reducing redundancy, improving accuracy, and improving efficiency

Inactive Publication Date: 2020-04-10
中国科学院电子学研究所苏州研究院
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Problems solved by technology

However, the traditional method identifies the key points of the trajectory by changing the direction of the trajectory data points or calculating the entropy value, and the data is inaccurate and redundant.
In addition, the similarity measurement

Method used

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  • Moving object track clustering method based on multi-dimensional distance measurement
  • Moving object track clustering method based on multi-dimensional distance measurement
  • Moving object track clustering method based on multi-dimensional distance measurement

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Experimental program
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Embodiment

[0052] In order to verify the effectiveness of the solution of the present invention, the following simulation experiments are carried out.

[0053] The trajectory clustering method of moving objects based on multidimensional distance measure, the specific implementation steps are as follows:

[0054] Input: Trajectory dataset TR = {TR 0 ,...,TR i ,...TR n ,0≤i≤n}, steering angle threshold θ d , speed change threshold V d , density radius ε and trajectory density threshold Min Lns .

[0055] Step1: Trajectory key point identification. For each track TR i ={P 0 ,P 1 ,...,P len} Carry out key point identification and generate key point set CP. Specific steps are as follows:

[0056] Step1.1: put P 0 and P len Join the set CP, set θ=θ + =ΔV=0.

[0057] Step1.2: For trajectory TR i point P in j , 1≤j≤len, calculate P j Steering angle θ at , accumulative steering angle θ + And the speed change value ΔV.

[0058] θ=Compute_Driction(P j-1 P j ,P j P j+1 ) (7)...

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Abstract

The invention discloses a moving object trajectory clustering method based on multi-dimensional distance measurement, which comprises the following steps: identifying key points from trajectory data,segmenting original trajectory data, and generating a trajectory segment set; constructing a multi-dimensional distance measurement function based on the spatial distance and the time distance, and calculating the distance between track segments; performing trajectory clustering by adopting a DBSCAN algorithm; and generating a representative trajectory based on a Sweep Line method. According to the method, the key points of the trajectory are identified according to the direction and speed dimension change values of the trajectory data points, so that the redundancy of original trajectory datais reduced, and the trajectory clustering efficiency is improved; and a multi-dimensional distance measurement function is constructed based on the spatial distance and the time distance, so that thetrack clustering precision is improved.

Description

technical field [0001] The present invention relates to track clustering technology, in particular to a moving object track clustering method based on multidimensional distance measurement. Background technique [0002] Clustering is based on the similarity between data, and divides those data with certain similarity together to form a cluster. Among them, the objects in the same cluster are similar to each other, and the objects in different clusters are different from each other. Clustering is different from classification, it is an unsupervised learning process, and the data can be divided into different clusters only according to the characteristics of the data itself. [0003] Trajectory clustering of moving objects refers to measuring the similarity between trajectories, and classifying trajectories with greater similarity into the same class to form different clusters. The trajectory clustering technology of moving objects has been continuously applied in various fi...

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Application Information

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IPC IPC(8): G06K9/62G06F16/29
CPCG06F16/29G06F18/2321
Inventor 杭谊青付琨练辉娟陈诗旭宋路杰
Owner 中国科学院电子学研究所苏州研究院
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