Trajectory Voting and Classification Based on Spatiotemporal Similarity in Moving Object Databases

Authors:
Costas Panagiotakis;Nikos Pelekis;Ioannis Kopanakis
Affiliations:
Dept. of Computer Science, University of Crete, Greece;Dept. of Informatics, University of Piraeus, Greece;E-Business Intelligence Lab, Dept. of Marketing, Technological Educational Institute of Crete, Greece
Venue:
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Year:
2009

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Abstract

We propose a method for trajectory classification based on trajectory voting in Moving Object Databases (MOD). Trajectory voting is performed based on local trajectory similarity. This is a relatively new topic in the spatial and spatiotemporal database literature with a variety of applications like trajectory summarization, classification, searching and retrieval. In this work, we have used moving object databases in space, acquiring spatiotemporal 3-D trajectories, consisting of the 2-D geographic location and the 1-D time information. Each trajectory is modelled by sequential 3-D line segments. The global voting method is applied for each segment of the trajectory, forming a local trajectory descriptor. By the analysis of this descriptor the representative paths of the trajectory can be detected, that can be used to visualize a MOD. Our experimental results verify that the proposed method efficiently classifies trajectories and their sub-trajectories based on a robust voting method.