An integrated space-time pattern classification approach for individuals' travel trajectories
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Clustering of trajectories in video surveillance using growing neural gas
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Mining trajectory corridors using fréchet distance and meshing grids
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Recently, the analysis of moving objects has become one of the most important technologies to be used in various applications such as GIS, navigation systems, and locationbased information systems, Existing geographic analysis approaches are based on points where each object is located at a certain time. These techniques can extract interesting motion patterns from each moving object, but they can not extract relative motion patterns from many moving objects. Therefore, to retrieve moving objects with a similar trajectory shape to another given moving object, we propose queries based on the similarity between the shapes of moving object trajectories. Our proposed technique can find trajectories whose shape is similar to a certain given trajectory. We define the shape-based similarity query trajectories as an extension of similarity queries for time series data, and then we propose a new clustering technique based on similarity by combining both velocities of moving objects and shapes of objects. Moreover, we show the effectiveness of our proposed clustering method through a performance study using moving rickshaw data.