Visual Multiple-Object Tracking for Unknown Clutter Rate
This work addresses a domain-specific challenge in visual tracking by handling unknown clutter rates, which is incremental as it builds on existing filter methods.
The paper tackled the problem of multi-object tracking with unknown false measurement rates by combining a robust multi-Bernoulli filter for clutter estimation with a generalized labeled multi-Bernoulli filter for target tracking, demonstrating effectiveness in real-world video scenarios.
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.