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UAST: Uncertainty-Aware Siamese Tracking
2022
International Conference on Machine Learning
Visual object tracking is basically formulated as target classification and bounding box estimation. Recent anchor-free Siamese trackers rely on predicting the distances to four sides for efficient regression but fail to estimate accurate bounding box in complex scenes. We argue that these approaches lack a clear probabilistic explanation, so it is desirable to model the uncertainty and ambiguity representation of target estimation. To address this issue, this paper presents an
dblp:conf/icml/0002FZ22
fatcat:dscqii6rpvfy5mr5skk6l5jnru