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Evaluating textual approximation to classify moving object trajectories

Classifying moving object trajectories in 2-dimensional space is a big challenge. Much research work has been performed on this field for many years. However, due to many factors such as sensor failures, noises, and sampling rates, it's very difficult to design a robust and fast method to retrieve or to do clustering these data. Textual Approximation is one of the methods for searching one-dimensional time series data, such as stock or electrocardiogram data, which has been proved to be more accurate on average than existing methods. The main idea behind Textual Approximation is to approximate time series data as a set of temporal terms to apply document retrieval methods. The main problem of applying Textual Approximation to multi-dimensional data, such as trajectory data and motion capture data, is how to extract temporal terms from multi-dimensional time series data. In this paper, we proposed an method employed Textual Approximation idea to classify moving object trajectories. Our method proposes a method to classify moving object trajectories in 2-dimensional space, which employ Textual Approximation idea. Our experiment results confirmed that our method achieved both performance and accuracy, compare to existing methods.