MHT-X: Offline Multiple Hypothesis Tracking with Algorithm X

TL;DR

This paper presents MHT-X, an open-source Python implementation of offline multiple hypothesis tracking using Algorithm X, designed for scientific applications like multiphase hydrodynamics, capable of resolving complex object trajectories.

Contribution

The paper introduces a versatile, extendable Python framework for offline multiple hypothesis tracking with Algorithm X, tailored for scientific applications involving complex object motion.

Findings

Successfully tracks multiphase hydrodynamics objects

Handles merges and splits in trajectories

Compatible with n-dimensional motion properties

Abstract

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.