We have recently published a paper on the use of Machine Learning to annotate trajectories of moving objects. There is an increasing amount of trajectories data becoming available by the tracking of various moving objects: animals, vessels, vehicles, and humans. However, these large collections of movement data lack semantic annotations, since they are typically done by domain experts in a time-consuming activity. We have shown that Machine Learning techniques can be an effective and efficient tool in the process of semantic annotation of trajectories. Specifically, we demonstrate how active learning allows a minimal set of trajectories to be annotated, while preserving good performance measures.  We test several active learning strategies with three different trajectories datasets. The paper, ANALYTiC: An Active Learning System for Trajectory Classification, co-authored by Amilcar Soares, Research Associate with the Institute, Chiara Renso from the Italian National research Council (CNR) Lab in Pisa, and Stan Matwin, will appear in IEEE Computer Graphics and Applications. The paper describes the ANALYTiC platform, a web-based interactive tool to visually assist the user in the active learning process over trajectory data.

Link for testing the tool: http://bigdata2.research.cs.dal.ca:8084/