Visual multiple-object tracking for unknown clutter rate

Abstract

© The Institution of Engineering and Technology 2018. 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 study, the authors are interested in designing a multi-object tracking algorithm that handles unknown false measurement rate. The recently proposed robust multi-Bernoulli filter is employed for clutter estimation while generalised labelled multi-Bernoulli filter is considered for target tracking. Performance evaluation with real videos demonstrates the effectiveness of the tracking algorithm for real-world scenarios.