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Shape-Assisted Multimodal Person Re-Identification
Shape-Assisted Multimodal Person Re-Identification
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151508
- ISBN
- 9798382652702
- DDC
- 004
- 저자명
- Zhu, Haidong.
- 서명/저자
- Shape-Assisted Multimodal Person Re-Identification
- 발행사항
- [Sl] : University of Southern California, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 169 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- 주기사항
- Advisor: Nevatia, Ramakant.
- 학위논문주기
- Thesis (Ph.D.)--University of Southern California, 2024.
- 초록/해제
- 요약Finding the identity of a person from a non-overlapping input image or video, known as person reidentification, is a classic and important task in biometric understanding. Identifying the corresponding identity requires extracting the representations of the person and distinguishing them across different individuals, where the representation can be human appearance, specific walking patterns, and body shape. Each representation can be understood as a specific modality and has its own strengths and weaknesses, while different modalities can sometimes complement each other. Therefore, combining two or more modalities introduces a more robust system for person re-identification.In this thesis, we cover different combinations of biometric representations for whole-body person re-identification, including appearance, gait, and body shape. Appearance is one of the most widely used biometric signals as it provides abundant information. Gait, represented as skeleton or binary silhouette sequences, captures the walking patterns of a person. Different from other representations, 3-D shape complements the body information with external human body shape prior and enhances the appearance captured in the 2-D images. Body shape also provides a strong prior of the person and helps complete the body shape to deal with occlusions. We discuss the combination of different representations and biometric signals that leverage their strengths, along with a system using the three signals for person re-identification in the wild. As the current body shapes used for person re-identification are usually not accurate enough to provide a distinguishable signal, we further discuss the improvement of the representations and how they can be applied for downstream vision tasks, such as person identification.We begin with three works that explicitly extract and combine different modalities for re-identification, including two gait representations (silhouettes and skeletons), two different shape-related modalities (gait and 3-D body shape), and the additional use of appearance along with the two shape-related modalities. Although 3-D body shape offers invaluable external shape-related information that 2-D images lack, existing body shape representations often fall short in accuracy or demand extensive image data, which is unavailable for re-identification tasks. Following this, we explore the potential of using more accurate body shape to further improve the model and introduce two other methods for more accurate 3-D shape representation and reconstruction: Implicit Functions (IF) and Neural Radiance Fields (NeRF). Since a fine-grained representation is needed for downstream vision tasks, we discuss how to include more semantic representation to assist the training of the 3-D reconstruction model and how it can aid with a limited number of input views. Lastly, with the fine-grained representation, we discuss using them for body shape representation to enhance appearance for person re-identification. We conclude the thesis with potential future work for further improvements.
- 일반주제명
- Computer science
- 일반주제명
- Computer engineering
- 키워드
- Biometrics
- 키워드
- Neural rendering
- 기타저자
- University of Southern California Computer Science
- 기본자료저록
- Dissertations Abstracts International. 85-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211151508
■006m o d
■007cr#unu||||||||
■020 ▼a9798382652702
■035 ▼a(MiAaPQ)AAI31299317
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aZhu, Haidong.
■24510▼aShape-Assisted Multimodal Person Re-Identification
■260 ▼a[Sl]▼bUniversity of Southern California▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a169 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■500 ▼aAdvisor: Nevatia, Ramakant.
■5021 ▼aThesis (Ph.D.)--University of Southern California, 2024.
■520 ▼aFinding the identity of a person from a non-overlapping input image or video, known as person reidentification, is a classic and important task in biometric understanding. Identifying the corresponding identity requires extracting the representations of the person and distinguishing them across different individuals, where the representation can be human appearance, specific walking patterns, and body shape. Each representation can be understood as a specific modality and has its own strengths and weaknesses, while different modalities can sometimes complement each other. Therefore, combining two or more modalities introduces a more robust system for person re-identification.In this thesis, we cover different combinations of biometric representations for whole-body person re-identification, including appearance, gait, and body shape. Appearance is one of the most widely used biometric signals as it provides abundant information. Gait, represented as skeleton or binary silhouette sequences, captures the walking patterns of a person. Different from other representations, 3-D shape complements the body information with external human body shape prior and enhances the appearance captured in the 2-D images. Body shape also provides a strong prior of the person and helps complete the body shape to deal with occlusions. We discuss the combination of different representations and biometric signals that leverage their strengths, along with a system using the three signals for person re-identification in the wild. As the current body shapes used for person re-identification are usually not accurate enough to provide a distinguishable signal, we further discuss the improvement of the representations and how they can be applied for downstream vision tasks, such as person identification.We begin with three works that explicitly extract and combine different modalities for re-identification, including two gait representations (silhouettes and skeletons), two different shape-related modalities (gait and 3-D body shape), and the additional use of appearance along with the two shape-related modalities. Although 3-D body shape offers invaluable external shape-related information that 2-D images lack, existing body shape representations often fall short in accuracy or demand extensive image data, which is unavailable for re-identification tasks. Following this, we explore the potential of using more accurate body shape to further improve the model and introduce two other methods for more accurate 3-D shape representation and reconstruction: Implicit Functions (IF) and Neural Radiance Fields (NeRF). Since a fine-grained representation is needed for downstream vision tasks, we discuss how to include more semantic representation to assist the training of the 3-D reconstruction model and how it can aid with a limited number of input views. Lastly, with the fine-grained representation, we discuss using them for body shape representation to enhance appearance for person re-identification. We conclude the thesis with potential future work for further improvements.
■590 ▼aSchool code: 0208.
■650 4▼aComputer science
■650 4▼aComputer engineering
■653 ▼aBiometrics
■653 ▼aBody shape reconstruction
■653 ▼aNeural rendering
■653 ▼aPerson re-identification
■653 ▼aImplicit Functions
■690 ▼a0984
■690 ▼a0464
■71020▼aUniversity of Southern California▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0208
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161963▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
