Generic trajectory similarity measure based on user de ned similarity criteria

Nehal Magdy Saber


There has been a tremendous growth in movement data due to availability of devices that could be used to track movement of objects. Tracking an object gives rise to a sequence of points in time and space, called a trajectory. One of the main functions is evaluating similarity between moving objects' trajectories and it has gained much attention in many application domains. There exist similarity measures in the literature that propose evaluating similarity between trajectories in the form of time stamped values, de nes some meaning of similarity and propose algorithms for computing it. The user is restricted to that meaning of similarity while it should be application dependent and only determined by the user. Therefore, there is a lack of genericness where there is a need for a generic approach where users can de ne the meaning of similarity. In this thesis, a new parametrized similarity operator, TWEDistance, is proposed. This operator is based on one of the discrete similarity measures, time warp edit distance, where the meaning of similarity is generic and left for user to de ne and it is implemented in Secondo. The similarity measures in the literature that are based on the discrete form of a trajectory is also a ected by the sampling rate differences as it is de ned over sequences of points. Therefore,this thesis propose to deal with the nature of trajectory's data as a continuous function. Continuous based similarity is evaluated using interpolation, regression and curve barcoding.

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Other Titles مقياس عام لتشابه المسارات على أساس تعريف المستخدم لمعنى التشابه
Issue Date 2017

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