MHT-X: Offline Multiple Hypothesis Tracking with Algorithm X
This work provides an open-source, versatile offline tracking tool for scientists in fields requiring detailed analysis of object motion, merges, and splits, particularly in multiphase hydrodynamics.
The authors developed MHT-X, an offline multiple hypothesis tracking algorithm using Algorithm X for optimal association search. It is implemented in Python and designed for scientific applications like bubble and particle tracking in multiphase hydrodynamics, capable of resolving object motion, merges, and splits.
An efficient and versatile implementation of offline multiple hypothesis tracking with Algorithm X for optimal association search was developed using Python. The code is intended for scientific applications that do not require online processing. Directed graph framework is used and multiple scans with progressively increasing time window width are used for edge construction for maximum likelihood trajectories. The current version of the code was developed for applications in multiphase hydrodynamics, e.g. bubble and particle tracking, and is capable of resolving object motion, merges and splits. Feasible object associations and trajectory graph edge likelihoods are determined using weak mass and momentum conservation laws translated to statistical functions for object properties. The code is compatible with n-dimensional motion with arbitrarily many tracked object properties. This framework is easily extendable beyond the present application by replacing the currently used heuristics with ones more appropriate for the problem at hand. The code is open-source and will be continuously developed further.