Learning Trajectory Patterns via Canonical Correlation Analysis

Abstract

A substantial body of research has been devoted to the analysis of motion trajectories. Usually, a motion trajectory consists of a set of coordinates, which is called a raw trajectory. In this paper, the authors first use vectors for some artificially constructed global features, such as the mean discrete curvature and standard deviation of acceleration, to represent the raw trajectory data, and then apply a multiset canonical correlation analysis method to extract latent features from the artificially constructed features. The performance of the latent features is then measured by evaluating the accuracy and F1 score of a gradient boosting decision tree model for different datasets, which include paired sample datasets and unpaired sample datasets. The experimental results show that the classifier performance for MCCA features is much better than that obtained for the artificially constructed features, such as that for the motion distance or mean velocity.