Object tracking based on multiple measurement hypotheses

Abstract

A method and system for integrating multiple measurement hypotheses in an efficient labeled multi-Bernoulli (LMB) filter. The LMB filter estimates a plurality of tracks for a plurality of objects, each track of the plurality of tracks having a unique label, a probability, and a state, wherein each track of the plurality of tracks is associated to an object of a plurality of objects to be tracked, each object having an object state. The method receives one or more measurement hypotheses of the multiple measurement hypotheses for each object of the plurality of objects; updates each track of the plurality of tracks based on the respective track and the one or more measurement hypotheses of the multiple measurement hypotheses; determines, for each combination of track of the plurality of tracks and measurement hypothesis, a likelihood η(j, k); samples, for each iteration of a plurality of iterations, an update hypothesis γ, based on an association of each track of the plurality of tracks to one of: a measurement hypothesis, an events missed detection, or a track dying detection; determining the state of each track of the plurality of tracks based on its respective associations in the updated hypotheses γ; extracts, for each track of the plurality of tracks, an existence probability; predicting the object state of each object of the plurality of objects with respect to a next measurement time; determines, whether another update is to be performed; and if another update is to be performed, repeats again the method steps from and including updating each track of the plurality of tracks.