Identification of Moving Vehicle Trajectory Using Manifold Learning

Abstract

We present a method to identify the trajectories of moving vehicles from various viewpoints using manifold learning to be implemented on an embedded platform for traffic surveillance. We use a robust kernel Isomap to estimate the intrinsic low-dimensional manifold of input space. During training, the extracted features of the training data are projected on to a 2D manifold and features corresponding to each trajectory are clustered in to k clusters, each represented as a Gaussian model. During identification, features of test data are projected on to the 2D manifold constructed during training and the Mahalanobis distance between test data and Gaussian models of each trajectory is evaluated to identify the trajectory. Experimental results demonstrate the effectiveness of the proposed method in estimating the trajectories of the moving vehicles, even though shapes and sizes of vehicles change rapidly.