MHT-X: offline multiple hypothesis tracking with algorithm X

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 scientific applications that do not require online processing. Directed graph framework used scans progressively increasing time window width are edge construction maximum likelihood trajectories. current version the in multiphase hydrodynamics, e.g. bubble particle tracking, capable resolving object motion, merges splits. Feasible associations trajectory likelihoods determined weak mass momentum conservation laws translated to statistical functions properties. compatible n-dimensional motion arbitrarily many tracked This easily extendable beyond present application by replacing currently heuristics ones more appropriate problem at hand. open-source will be continuously further.