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

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...

AI Technical Summary

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 function between trajectory segments is mainly constructed for one or two trajectory attributes, resulting in low quality trajectory clustering, and the obtained clustering results cannot fully reflect the motion characteristics of the trajectory.

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