Multi-Object Tracking with Multiple Birth, Death, and Spawn Scenarios Using A Randomized Hypothesis Generation Technique (R-FISST)
For researchers in multi-object tracking, this work provides a computationally tractable method for handling complex birth, death, and spawn scenarios, though it is an incremental improvement over prior MCMC-based FISST methods.
The paper tackles the computational intractability of hypothesis generation in multi-object tracking with multiple birth, death, and spawn events. The proposed R-FISST method uses MCMC sampling to handle these scenarios without exhaustive enumeration, demonstrating effectiveness on Space Situational Awareness scenarios with spawn events.
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.