  Quantitative analysis of the dynamics of tiny cellular and sub-cellular
structures, known as particles, in time-lapse cell microscopy sequences
requires the development of a reliable multi-target tracking method capable of
tracking numerous similar targets in the presence of high levels of noise, high
target density, complex motion patterns and intricate interactions. In this
paper, we propose a framework for tracking these structures based on the random
finite set Bayesian filtering framework. We focus on challenging biological
applications where image characteristics such as noise and background intensity
change during the acquisition process. Under these conditions, detection
methods usually fail to detect all particles and are often followed by missed
detections and many spurious measurements with unknown and time-varying rates.
To deal with this, we propose a bootstrap filter composed of an estimator and a
tracker. The estimator adaptively estimates the required meta parameters for
the tracker such as clutter rate and the detection probability of the targets,
while the tracker estimates the state of the targets. Our results show that the
proposed approach can outperform state-of-the-art particle trackers on both
synthetic and real data in this regime.
