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Robust Track-to-Track Association Algorithm for Large Sensor Bias and Dense Objects
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DOI：10.1109/LSP.2026.3662609.png)
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Abstract
En 中文
In order to cope with the track-to-track association (T2TA) problem under large sensor bias, and dense objects, this letter proposes a T2TA algorithm based on two alternating triangle and translation local track feature descriptors (TFDs). The proposed TFDs are constructed by pseudo-correspondence, triangle area and angle, translation vector, and Euclidean distance, and are combined via a transition algorithm that enables them alternating operation, to ensure the real-time performance of the feature extraction process. Finally, we establish the T2TA algorithm through a combination of global track feature, linear assignment algorithm, thin plate spline function, and simulated annealing algorithm. Experiments demonstrate the significant advantages of our proposed TFDs and T2TA algorithm compared with state-of-the-art algorithms under large sensor bias, and dense objects.
Keywords:
Track-to-track association
large sensor bias
dense objects
track feature descriptor
Journal
I
IF:
3.9
Papers:
610
Citations:
0