  Extended target tracking estimates the centroid and shape of the target in
space and time. In various situations where extended target tracking is
applicable, the presence of multiple targets can lead to interference,
particularly when they maneuver behind one another in a sensor like a camera.
Nonetheless, when dealing with multiple extended targets, there's a tendency
for them to share similar shapes within a group, which can enhance their
detectability. For instance, the coordinated movement of a cluster of aerial
vehicles might cause radar misdetections during their convergence or
divergence. Similarly, in the context of a self-driving car, lane markings
might split or converge, resulting in inaccurate lane tracking detections. A
well-known joint probabilistic data association coupled (JPDAC) filter can
address this problem in only a single-point target tracking. A variation of
JPDACF was developed by introducing a nonparametric Spatio-Temporal Joint
Probabilistic Data Association Coupled Filter (ST-JPDACF) to address the
problem for extended targets. Using different kernel functions, we manage the
dependency of measurements in space (inside a frame) and time (between frames).
Kernel functions are able to be learned using a limited number of training
data. This extension can be used for tracking the shape and dynamics of
nonparametric dependent extended targets in clutter when targets share
measurements. The proposed algorithm was compared with other well-known
supervised methods in the interfering case and achieved promising results.
