# Bayesian Nonparametric View to Spawning

arXiv: https://arxiv.org/abs/2112.06640 · HTML: https://mlsanity.com/paper/2112.06640/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 3 Dec 2021.

## Builds on (most cited first, 3 of 3)

- [Inference for multiple object tracking: A Bayesian nonparametric approach](https://mlsanity.com/paper/1909.06984/) (2019)
- [Multi-Object Tracking with Multiple Birth, Death, and Spawn Scenarios Using A Randomized Hypothesis Generation Technique (R-FISST)](https://mlsanity.com/paper/1603.07684/) (2016)
- [Bayesian nonparametric modeling for predicting dynamic dependencies in multiple object tracking](https://mlsanity.com/paper/2004.10798/) (2020)

## Abstract (arXiv)

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.