Orthogonal Single-Target Tracking

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초록

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.

키워드

Visualization; Target tracking; Probability distribution; Monte Carlo methods; Feature extraction; Neural networks; Computational efficiency; Computer vision; distance measurement; probability distribution; NETWORKS
제목
Orthogonal Single-Target Tracking
저자
Kim, Youjin; Kwon, Junseok
DOI
10.1109/ACCESS.2022.3162200
발행일
2022-03
유형
Article
저널명
IEEE Access
권
10
페이지
33527 ~ 33536

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