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Orthogonal Single-Target Tracking
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AI
DOI：10.1109/ACCESS.2022.3162200.png)
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Abstract
En 中文
In this study, we propose a novel Wasserstein distributional tracking method that can balance approximation with accuracy in terms of Monte Carlo estimation. To achieve this goal, we present three different systems: sliced Wasserstein-based (SWT), projected Wasserstein-based (PWT), and orthogonal coupled Wasserstein-based (OCWT) visual tracking systems. Sliced Wasserstein-based visual trackers can find accurate target configurations using the optimal transport plan, which minimizes the discrepancy between appearance distributions described by the estimated and ground truth configurations. Because this plan involves a finite number of probability distributions, the computation costs can be considerably reduced. Projected Wasserstein-based and orthogonal coupled Wasserstein-based visual trackers further enhance the accuracy of visual trackers using bijective mapping functions and orthogonal Monte Carlo, respectively. Experimental results demonstrate that our approach can balance computational efficiency with accuracy, and the proposed visual trackers outperform other state-of-the-art visual trackers on several benchmark visual tracking datasets.
Keywords:
Visualization
Target tracking
Probability distribution
Monte Carlo methods
Feature extraction
Neural networks
Computational efficiency
Computer vision
distance measurement
probability distribution
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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