Approximate evaluation of marginal association probabilities with belief propagation
This addresses the fundamental problem of data association in tracking, providing an incremental improvement with proven convergence and efficiency gains.
The paper tackles the data association problem in tracking by formulating it as a graphical model and applying belief propagation to estimate marginal association probabilities, proving convergence and bounding iterations, with experiments showing favorable accuracy and computational complexity compared to prior methods.
Data association, the problem of reasoning over correspondence between targets and measurements, is a fundamental problem in tracking. This paper presents a graphical model formulation of data association and applies an approximate inference method, belief propagation (BP), to obtain estimates of marginal association probabilities. We prove that BP is guaranteed to converge, and bound the number of iterations necessary. Experiments reveal a favourable comparison to prior methods in terms of accuracy and computational complexity.