This simulation demonstrates multiple object tracking using particle
techniques. In this simulation, we have three dinghies (shown in the
frame), each with a 7-dimensional state space: x- and y-location,
x- and y-velocities, change in orientation, and motion type. Each
three possible motion types: adrift, rowing, or motorized, and switches
these three as a Markov chain. Further, the dinghies interact by
when far apart, and repelling when close together.
The observations (top-middle frame) which we get are similar to what we
get when observing the ocean surface from above using an infrared camera.
Each pixel is corrupted by Gaussian noise, with a slightly higher mean
intensity of the pixel coincides with a dinghy's position.
At the bottom, we see the output of the filter. The three frames
three possible numbers of targets. There is a fourth possibility,
having no targets, which is not displayed. The filter does not know,
beforehand, the correct number of targets. The green bar at the
bottom of each
of the frames represents the probability for each of the possible numbers
targets. Each frame shows an approximated density for the dinghies'
as a heat-scale, conditioned on the number of targets associated with that