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Long-term Vision-Based Autonomous Underwater Target Tracking
Long-term Vision-Based Autonomous Underwater Target Tracking
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152115
- ISBN
- 9798384345046
- DDC
- 001
- 저자명
- Zhang, Miao.
- 서명/저자
- Long-term Vision-Based Autonomous Underwater Target Tracking
- 발행사항
- [Sl] : Stanford University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 141 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
- 주기사항
- Advisor: Rock, Stephen.
- 학위논문주기
- Thesis (Ph.D.)--Stanford University, 2024.
- 초록/해제
- 요약This thesis introduces T-STORE (a long-term tracker with dynamic DCF Template STORE(-age) for target recovery), an algorithm designed to enable long-duration vision-based tracking of highly deformable ocean midwater animals using autonomous underwater vehicles.Current midwater tracking practices typically employ stereo blob tracking algorithms to accomplish this task. These systems have proven to be highly effective when the target animal remains within the camera's field-of-view, achieving tracking durations of multiple hours. These systems fail, however, when disruptions occur, such as the target temporarily moving out of view or being occluded by other objects. As a result, they are unsuited for applications requiring tracking durations greater than 24 hours. Addressing the challenge of target re-identification post-disruptions is essential for extending tracking durations.T-STORE presents a solution to the target re-identification problem by fusing the stereo blob tracking system with a visual template-based learning system built on a pool of online-acquired Discriminative Correlation Filer (DCF) templates. Instead of employing an offline-trained Convolutional Neural Network (CNN) detector, T-STORE utilizes an online template-based learning approach to address the scarcity of large-scale data on midwater targets and to enable the tracking of previously unseen targets-of-opportunity.T-STORE is the first to integrate stereo blob tracking with Discriminative Correlation Filters (DCFs). It introduces a novel target re-identification pipeline, leveraging a unique template matching metric and exploiting target information collected during online learning. Additionally, T-STORE introduces a set of lightweight deep features optimized for DCF-based template matching, ensuring adaptability to appearance changes and reducing the required number of templates, which in turn enhances suitability for real-time applications.The performance of T-STORE is demonstrated using field data containing challenging tracking conditions that lead to failures in the stereo blob tracking algorithm. The experiments include a comparison with FuCoLoT (a Fully Correlational Long-Term Tracker), an accepted leader in DCF template-based tracking algorithms. T-STORE outperformed FuCoLoT in both standard tracking metrics and target recovery performance. Specifically, T-STORE exhibited a target recovery success rate exceeding 80%, double that of FuCoLoT, along with an overall F-score of over 0.8, indicating its promising potential for achieving the long duration tracking goal.
- 일반주제명
- Visualization
- 일반주제명
- Decision making
- 일반주제명
- Naval engineering
- 일반주제명
- Aerospace engineering
- 기타저자
- Stanford University.
- 기본자료저록
- Dissertations Abstracts International. 86-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152115
■006m o d
■007cr#unu||||||||
■020 ▼a9798384345046
■035 ▼a(MiAaPQ)AAI31460282
■035 ▼a(MiAaPQ)Stanfordgw611hn7687
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a001
■1001 ▼aZhang, Miao.
■24510▼aLong-term Vision-Based Autonomous Underwater Target Tracking
■260 ▼a[Sl]▼bStanford University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a141 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: B.
■500 ▼aAdvisor: Rock, Stephen.
■5021 ▼aThesis (Ph.D.)--Stanford University, 2024.
■520 ▼aThis thesis introduces T-STORE (a long-term tracker with dynamic DCF Template STORE(-age) for target recovery), an algorithm designed to enable long-duration vision-based tracking of highly deformable ocean midwater animals using autonomous underwater vehicles.Current midwater tracking practices typically employ stereo blob tracking algorithms to accomplish this task. These systems have proven to be highly effective when the target animal remains within the camera's field-of-view, achieving tracking durations of multiple hours. These systems fail, however, when disruptions occur, such as the target temporarily moving out of view or being occluded by other objects. As a result, they are unsuited for applications requiring tracking durations greater than 24 hours. Addressing the challenge of target re-identification post-disruptions is essential for extending tracking durations.T-STORE presents a solution to the target re-identification problem by fusing the stereo blob tracking system with a visual template-based learning system built on a pool of online-acquired Discriminative Correlation Filer (DCF) templates. Instead of employing an offline-trained Convolutional Neural Network (CNN) detector, T-STORE utilizes an online template-based learning approach to address the scarcity of large-scale data on midwater targets and to enable the tracking of previously unseen targets-of-opportunity.T-STORE is the first to integrate stereo blob tracking with Discriminative Correlation Filters (DCFs). It introduces a novel target re-identification pipeline, leveraging a unique template matching metric and exploiting target information collected during online learning. Additionally, T-STORE introduces a set of lightweight deep features optimized for DCF-based template matching, ensuring adaptability to appearance changes and reducing the required number of templates, which in turn enhances suitability for real-time applications.The performance of T-STORE is demonstrated using field data containing challenging tracking conditions that lead to failures in the stereo blob tracking algorithm. The experiments include a comparison with FuCoLoT (a Fully Correlational Long-Term Tracker), an accepted leader in DCF template-based tracking algorithms. T-STORE outperformed FuCoLoT in both standard tracking metrics and target recovery performance. Specifically, T-STORE exhibited a target recovery success rate exceeding 80%, double that of FuCoLoT, along with an overall F-score of over 0.8, indicating its promising potential for achieving the long duration tracking goal.
■590 ▼aSchool code: 0212.
■650 4▼aVisualization
■650 4▼aDecision making
■650 4▼aAutonomous underwater vehicles
■650 4▼aNaval engineering
■650 4▼aAerospace engineering
■690 ▼a0468
■690 ▼a0538
■71020▼aStanford University.
■7730 ▼tDissertations Abstracts International▼g86-04B.
■790 ▼a0212
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162943▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
