  Due to its rising importance in science and technology in recent years,
particle tracking in videos presents itself as a tool for successfully
acquiring new knowledge in the field of life sciences and physics. Accordingly,
different particle tracking methods for various scenarios have been developed.
In this article, we present a particle tracking application implemented in
Python for, in particular, spherical magnetic particles, including
superparamagnetic beads and Janus particles. In the following, we distinguish
between two sub-steps in particle tracking, namely the localization of
particles in single images and the linking of the extracted particle positions
of the subsequent frames into trajectories. We provide an intensity-based
localization technique to detect particles and two linking algorithms, which
apply either frame-by-frame linking or linear assignment problem solving.
Beyond that, we offer helpful tools to preprocess images automatically as well
as estimate parameters required for the localization algorithm by utilizing
machine learning. As an extra, we have implemented a technique to estimate the
current spatial orientation of Janus particles within the x-y-plane. Our
framework is readily extendable and easy-to-use as we offer a graphical user
interface and a command-line tool. Various output options, such as data frames
and videos, ensure further analysis that can be automated.
