Data-association-free Characterization of Labeling Uncertainty: The Cross Modeling Tracker

Key Points

• Labeling uncertainty significantly affects tracking performance across multiple modeling scenarios.
• The proposed cross modeling tracker enhances accuracy by eliminating data-association dependencies.
• This analysis uses advanced techniques to characterize labeling uncertainty in real-time tracking systems.
• Results may enable better tracking solutions in various applications, including robotics and surveillance.

Abstract