  Multi-hypothesis tracking is a flexible and intuitive approach to tracking
multiple nearby objects. However, the original formulation of its data
association step is widely thought to scale poorly with the number of tracked
objects. We propose enhancements including handling undetected objects and
false measurements without inflating the size of the problem, early stopping
during solution calculation, and providing for sparse or gated input. These
changes collectively improve the computational time and space requirements of
data association so that hundreds or thousands of hypotheses over hundreds of
objects may be considered in real time. A multi-sensor simulation demonstrates
that scaling up the hypothesis count can significantly improve performance in
some applications.
