Visual Multiple-Object Tracking for Unknown Clutter Rate

In most multi-object tracking algorithms, tuning of model parameters is of critical importance for reliable performance. In particular, we are interested in designing a robust tracking algorithm that is able to handle unknown false measurement rate. The proposed algorithm is based on coupling of two random finite set filters that share tracking parameters. Performance evaluation with visual surveillance and cell microscopy images demonstrates the effectiveness of the tracking algorithm for real-world scenarios.