  In multi-object tracking applications, model parameter tuning is a
prerequisite for reliable performance. In particular, it is difficult to know
statistics of false measurements due to various sensing conditions and changes
in the field of views. In this paper we are interested in designing a
multi-object tracking algorithm that handles unknown false measurement rate.
Recently proposed robust multi-Bernoulli filter is employed for clutter
estimation while generalized labeled multi-Bernoulli filter is considered for
target tracking. Performance evaluation with real videos demonstrates the
effectiveness of the tracking algorithm for real-world scenarios.
