Multi-Object Tracking with Multiple Birth, Death, and Spawn Scenarios Using A Randomized Hypothesis Generation Technique (R-FISST)

TL;DR

This paper introduces R-FISST, a randomized hypothesis generation technique for multi-object tracking that efficiently handles multiple birth, death, and spawn scenarios using MCMC sampling, improving computational tractability.

Contribution

It develops a novel randomized approach based on FISST that accounts for complex birth and death hypotheses in multi-object tracking, especially with spawn events.

Findings

Successfully tested on SSA scenarios with spawn events

Handles multiple birth and death hypotheses efficiently

Reduces computational complexity in hypothesis generation

Abstract

In multi-object tracking one may encounter situations were at any time step the number of possible hypotheses is too large to generate exhaustively. These situations generally occur when there are multiple ambiguous measurement returns that can be associated to many objects. This paper contains a newly developed approach that keeps the aforementioned situations computationally tractable. Utilizing a hypothesis level derivation of the Finite Set Statistics (FISST) Bayesian recursions for multi-object tracking we are able to propose a randomized method called randomized FISST (R-FISST). Like our previous methods, this approach utilizes Markov Chain Monte Carlo (MCMC) methods to sample highly probable hypotheses, however, the newly developed (R-FISST) can account for hypotheses containing multiple births and death within the MCMC sampling. This alleviates the burden of having to exhaustively enumerate all birth and death hypotheses and makes the method more equipped to handle spawn scenarios. We test our method on Space Situational Awareness (SSA) scenarios with spawn events.