Image-based Range Estimation of a Moving Target using Gaussian Process Motion Models

This paper presents a novel range estimation of moving targets observed by a moving camera. The target motions are modeled using Gaussian Processes (GP). Using GP regression, target velocity models of several basic motions are learned a-priori and stored as a library. An interacting multiple model (IMM) filter is then utilized on the perspective dynamical system (PDS) model to estimate the 3D range of the feature points on the moving target. The IMM selects the most likely target motion model from the bank of motion models such that the measurement likelihood is maximized and the relative range state is estimated from the image observations. Simulation results performed using a target motion that is a combination of basic target motions show good range estimation performance in terms of the root mean square error (RMSE) metric.