SOTA: Sequential Optimal Transport Approximation for Visual Tracking in Wild Scenario
In this study, we introduce a probabilistic visual tracking method tailored for wild scenarios, where tracking environments experience abrupt changes over time. In probabilistic visual tracking, particularly when utilizing sequential Monte Carlo (MC) sampling, the careful choice of a proposal functi...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10766609/ |
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| author | Seonghak Lee Jisoo Park Radu Timofte Junseok Kwon |
| author_facet | Seonghak Lee Jisoo Park Radu Timofte Junseok Kwon |
| author_sort | Seonghak Lee |
| collection | DOAJ |
| description | In this study, we introduce a probabilistic visual tracking method tailored for wild scenarios, where tracking environments experience abrupt changes over time. In probabilistic visual tracking, particularly when utilizing sequential Monte Carlo (MC) sampling, the careful choice of a proposal function is critical for attaining precise tracking results, where optimal transport techniques can assist in reducing the variance from important sampling and inducing a suitable proposal function. However, if a tracked object undergoes significant changes in appearance over time, it creates a large discrepancy between the proposal function and its target distribution. This situation presents the topological and representational challenges for conventional optimal transport techniques. To address this problem, the proposed visual tracker leverages the benefits of both MC sampling and optimal transport and presents the Sequential Optimal Transport Approximation (SOTA) visual tracker. For this, our method involves transforming the proposal function into its target distribution across successive temperature steps through sequential MC sampling. These MC steps can reduce the topological and representational burden on the optimal transport. Within each successive temperature step, one distribution is projected onto another one via optimal transport. By using optimal transport, the method can mitigate the variance from importance sampling. The experimental results demonstrate that the proposed method considerably outperforms other state-of-the-art methods across several benchmark dataset, particularly in tracking environments where abrupt changes occur. |
| format | Article |
| id | doaj-art-e2e5ef5b67de4c5db79c2543723469ad |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e2e5ef5b67de4c5db79c2543723469ad2025-08-20T03:08:40ZengIEEEIEEE Access2169-35362024-01-011217702817703710.1109/ACCESS.2024.350585610766609SOTA: Sequential Optimal Transport Approximation for Visual Tracking in Wild ScenarioSeonghak Lee0Jisoo Park1Radu Timofte2https://orcid.org/0000-0002-1478-0402Junseok Kwon3https://orcid.org/0000-0001-9526-7549School of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaGraduate School of Artificial Intelligence, Chung-Ang University, Seoul, South KoreaComputer Vision Laboratory, CAIDAS & IFI, University of Würzburg, Würzburg, GermanySchool of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaIn this study, we introduce a probabilistic visual tracking method tailored for wild scenarios, where tracking environments experience abrupt changes over time. In probabilistic visual tracking, particularly when utilizing sequential Monte Carlo (MC) sampling, the careful choice of a proposal function is critical for attaining precise tracking results, where optimal transport techniques can assist in reducing the variance from important sampling and inducing a suitable proposal function. However, if a tracked object undergoes significant changes in appearance over time, it creates a large discrepancy between the proposal function and its target distribution. This situation presents the topological and representational challenges for conventional optimal transport techniques. To address this problem, the proposed visual tracker leverages the benefits of both MC sampling and optimal transport and presents the Sequential Optimal Transport Approximation (SOTA) visual tracker. For this, our method involves transforming the proposal function into its target distribution across successive temperature steps through sequential MC sampling. These MC steps can reduce the topological and representational burden on the optimal transport. Within each successive temperature step, one distribution is projected onto another one via optimal transport. By using optimal transport, the method can mitigate the variance from importance sampling. The experimental results demonstrate that the proposed method considerably outperforms other state-of-the-art methods across several benchmark dataset, particularly in tracking environments where abrupt changes occur.https://ieeexplore.ieee.org/document/10766609/Visual trackingoptimal transportsequential Monte Carlo |
| spellingShingle | Seonghak Lee Jisoo Park Radu Timofte Junseok Kwon SOTA: Sequential Optimal Transport Approximation for Visual Tracking in Wild Scenario IEEE Access Visual tracking optimal transport sequential Monte Carlo |
| title | SOTA: Sequential Optimal Transport Approximation for Visual Tracking in Wild Scenario |
| title_full | SOTA: Sequential Optimal Transport Approximation for Visual Tracking in Wild Scenario |
| title_fullStr | SOTA: Sequential Optimal Transport Approximation for Visual Tracking in Wild Scenario |
| title_full_unstemmed | SOTA: Sequential Optimal Transport Approximation for Visual Tracking in Wild Scenario |
| title_short | SOTA: Sequential Optimal Transport Approximation for Visual Tracking in Wild Scenario |
| title_sort | sota sequential optimal transport approximation for visual tracking in wild scenario |
| topic | Visual tracking optimal transport sequential Monte Carlo |
| url | https://ieeexplore.ieee.org/document/10766609/ |
| work_keys_str_mv | AT seonghaklee sotasequentialoptimaltransportapproximationforvisualtrackinginwildscenario AT jisoopark sotasequentialoptimaltransportapproximationforvisualtrackinginwildscenario AT radutimofte sotasequentialoptimaltransportapproximationforvisualtrackinginwildscenario AT junseokkwon sotasequentialoptimaltransportapproximationforvisualtrackinginwildscenario |