6533b7d7fe1ef96bd1268ea8
RESEARCH PRODUCT
MHT-X: Offline Multiple Hypothesis Tracking with Algorithm X
Peteris ZvejnieksMihails BirjukovsMartins KlevsMegumi AkashiSven EckertAndris Jakovicssubject
Fluid Flow and Transfer ProcessesFOS: Computer and information sciencesbubble dynamicsComputer Vision and Pattern Recognition (cs.CV)neutron imagingComputational MechanicsComputer Science - Computer Vision and Pattern RecognitionFluid Dynamics (physics.flu-dyn)General Physics and AstronomyFOS: Physical sciencesPhysics - Fluid DynamicsAlgorithm Ximage processingtwo-phase flowMechanics of Materialsliquid metalX-ray radiographydescription
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.
year | journal | country | edition | language |
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2021-01-01 |