Human Pose Estimation: Single-Person and Multi-Person Approaches
Human pose estimation (HPE), as one of the core tasks in computer vision, plays a crucial role in enabling computers to comprehend human behaviour interactions. With the advancement of technology, this task has demonstrated significant potential in various application areas such as motion capture, b...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02019.pdf |
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| _version_ | 1825206573095452672 |
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| author | Tang Wan |
| author_facet | Tang Wan |
| author_sort | Tang Wan |
| collection | DOAJ |
| description | Human pose estimation (HPE), as one of the core tasks in computer vision, plays a crucial role in enabling computers to comprehend human behaviour interactions. With the advancement of technology, this task has demonstrated significant potential in various application areas such as motion capture, behavior analysis and augmented reality. Despite significant progress in recent years, HPE still presents challenges when dealing with complex scenarios such as occlusion, illumination changes, and dynamic backgrounds. This paper will provide a comprehensive overview of HPE techniques, focusing on both single-person and multi-person poses. According to their respective characteristics and application scenarios, single-person pose estimation is categorized into traditional methods and deep learning methods, while multi-person pose estimation is classified into top-down and bottom-up aspects. In addition, this paper analyzes commonly used datasets relevant to HPE, discusses the current unsolved issues, and forecasts future research directions, with the aim of providing valuable references and guidance for subsequent research in this field. |
| format | Article |
| id | doaj-art-82c7236e809d482f8f3ab89f314d1ea9 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-82c7236e809d482f8f3ab89f314d1ea92025-02-07T08:21:10ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700201910.1051/itmconf/20257002019itmconf_dai2024_02019Human Pose Estimation: Single-Person and Multi-Person ApproachesTang Wan0School of Mathematics and Statistics, South-Central Minzu UniversityHuman pose estimation (HPE), as one of the core tasks in computer vision, plays a crucial role in enabling computers to comprehend human behaviour interactions. With the advancement of technology, this task has demonstrated significant potential in various application areas such as motion capture, behavior analysis and augmented reality. Despite significant progress in recent years, HPE still presents challenges when dealing with complex scenarios such as occlusion, illumination changes, and dynamic backgrounds. This paper will provide a comprehensive overview of HPE techniques, focusing on both single-person and multi-person poses. According to their respective characteristics and application scenarios, single-person pose estimation is categorized into traditional methods and deep learning methods, while multi-person pose estimation is classified into top-down and bottom-up aspects. In addition, this paper analyzes commonly used datasets relevant to HPE, discusses the current unsolved issues, and forecasts future research directions, with the aim of providing valuable references and guidance for subsequent research in this field.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02019.pdf |
| spellingShingle | Tang Wan Human Pose Estimation: Single-Person and Multi-Person Approaches ITM Web of Conferences |
| title | Human Pose Estimation: Single-Person and Multi-Person Approaches |
| title_full | Human Pose Estimation: Single-Person and Multi-Person Approaches |
| title_fullStr | Human Pose Estimation: Single-Person and Multi-Person Approaches |
| title_full_unstemmed | Human Pose Estimation: Single-Person and Multi-Person Approaches |
| title_short | Human Pose Estimation: Single-Person and Multi-Person Approaches |
| title_sort | human pose estimation single person and multi person approaches |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02019.pdf |
| work_keys_str_mv | AT tangwan humanposeestimationsinglepersonandmultipersonapproaches |