Clustering method for interest point tracks under multiple temporal and spatial characteristic fusion

Abstract

The invention discloses a clustering method for interest point tracks under multiple temporal and spatial characteristic fusion. The clustering method comprises the following steps: firstly, acquiring interest points where users stay according to the temporal and spatial span between continuous coordinate points of the tracks, and segmenting the tracks into a plurality of track segments through the interest points; secondly, calculating the difference of the tracks in the space, the time, the speed and the direction to comprehensively judge the similarity between two tracks; finally, performing density clustering on the tracks based on an OPTICS method, and cutting off clustering clusters with sparse track quantity. According to the clustering method disclosed by the invention, the tracks are converted into an interest point sequence; the tracks are clustered by comprehensively considering the speed, the direction and temporal and spatial characteristics of the track segment between every two continuous interest points, so a significant cluster is selected and further the track clustering form which can comprehensively reflect global importance is obtained. Provided by experimental results, the method retains original temporal and spatial characteristics and the moving attribute of the tracks, can comprehensively reflect the motion and behavior mode of a mobile object and is high in clustering accuracy.

Description

technical field [0001] The invention belongs to the field of trajectory clustering algorithms, in particular to a method for clustering trajectory of interest points under multi-temporal feature fusion. Background technique [0002] With the development of satellites, the Internet and tracking equipment, a large number of trajectory data of moving objects are captured, such as vehicle movement, animal movement, typhoon direction, personnel movement, etc. These large amounts of accumulated trajectory data record the position and time of moving objects, which contain rich spatiotemporal knowledge and have great application value. By analyzing trajectory data, it is helpful to conduct research in various fields such as human behavior patterns, transportation logistics, emergency evacuation management, animal behavior, marketing, computational geometry, and simulation. Through cluster analysis of various spatio-temporal trajectory data, the similarity and abnormal features in s...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the embodiments of the present invention is to provide a trajectory clustering method for points of interest under the fusion of multi-temporal and spatial features, aiming at solving the problem that the current trajectory clustering algorithm does not comprehensively consider the global and local spatiotemporal similarities of trajectories, resulting in urban activity trajectories Clustering is difficult to reflect the similarity of social activities

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