Tracking Dependent Extended Targets Using Multi-Output Spatiotemporal Gaussian Processes

In Extended Target Tracking, where estimating the shape is essential as kinematic, exploiting the dependencies between targets is often an excellent way to enhance performance. In a group of dependent targets, sampled features tend to have spatially and temporally correlations inside and between frames. Gaussian process regression has been used as a powerful Bayesian semi-supervised method to describe functions’ spatial and temporal correlation. This paper exploits and models the dependency between extended targets using Gaussian Process. We propose a novel recursive approach called Multi-Output Spatio-Temporal Gaussian Process Kalman Filter (MO-STGP-KF) to estimate and track multiple dependent extended targets that have possibly been degraded or covered with clutter. We used this method for detecting and tracking the group of connected lane markings called “lane-lines”. For detection and clustering, we propose a new Kernel-based Joint Probabilistic Data Association Coupled Filter (K-JPDACF) to cluster point features belonging to each lane-line. Compared to recently published model-based multi-lane tracking, semi-supervised, and fully supervised lane detection methods, our method shows 13 percent 34 percent and 20 percent improvement in accuracy, respectively.