Bayesian Nonparametric View to Spawning
This addresses a specific challenge in multi-object tracking for scenarios with spawning events, representing an incremental improvement.
The paper tackles the problem of tracking multiple objects when each observation may be associated with multiple objects (spawning), introducing a Bayesian nonparametric approach with a tractable MCMC method to sample from the posterior distribution, and shows experimental advantages over existing methods.
In tracking multiple objects, it is often assumed that each observation (measurement) is originated from one and only one object. However, we may encounter a situation that each measurement may or may not be associated with multiple objects at each time step --spawning. Therefore, the association of each measurement to multiple objects is a crucial task to perform in order to track multiple objects with birth and death. In this paper, we introduce a novel Bayesian nonparametric approach that models a scenario where each observation may be drawn from an unknown number of objects for which it provides a tractable Markov chain Monte Carlo (MCMC) approach to sample from the posterior distribution. The number of objects at each time step, itself, is also assumed to be unknown. We, then, show through experiments the advantage of nonparametric modeling to scenarios with spawning events. Our experiment results also demonstrate the advantages of our framework over the existing methods.