Lossless Processing and the Limits of Trackability in MHT
Andrew Hunter, Stefano P. Coraluppi, Brandon Bale · 2023
Practical multi-hypothesis trackers (MHTs) often entail a number of parameters for track confirmation and extraction logic, gating and pruning, most of which are chosen heuristically to tradeoff performance and computational cost. Conceptually, these parameters are unnecessary with optimal MHT processing, as these decisions will fall out from the optimal solution, though perhaps with an increase in processing cost. We demonstrate, however, with a canonical MHT model and its attendant association assignment problem that many of these parameters can be chosen losslessly, that is, they only remove hypotheses that an optimal association solution is guaranteed to remove anyway, and thus strictly improve computational cost, with no loss in tracking performance. At the other end of the spectrum, the tools developed likewise yield a number of relations that detect when parameters are set such that practical tracking is no longer possible.