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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.18977 |
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| _version_ | 1866910723940024320 |
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| author | Wang, Zhiting Zhou, Qiangong Liu, Zongyang |
| author_facet | Wang, Zhiting Zhou, Qiangong Liu, Zongyang |
| contents | Segment Anything Model 2 (SAM2) demonstrates exceptional performance in video segmentation and refinement of segmentation results. We anticipate that it can further evolve to achieve higher levels of automation for practical applications. Building upon SAM2, we conducted a series of practices that ultimately led to the development of a fully automated pipeline, termed Det-SAM2, in which object prompts are automatically generated by a detection model to facilitate inference and refinement by SAM2. This pipeline enables inference on infinitely long video streams with constant VRAM and RAM usage, all while preserving the same efficiency and accuracy as the original SAM2.
This technical report focuses on the construction of the overall Det-SAM2 framework and the subsequent engineering optimization applied to SAM2. We present a case demonstrating an application built on the Det-SAM2 framework: AI refereeing in a billiards scenario, derived from our business context. The project at \url{https://github.com/motern88/Det-SAM2}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18977 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Det-SAM2:Technical Report on the Self-Prompting Segmentation Framework Based on Segment Anything Model 2 Wang, Zhiting Zhou, Qiangong Liu, Zongyang Computer Vision and Pattern Recognition Segment Anything Model 2 (SAM2) demonstrates exceptional performance in video segmentation and refinement of segmentation results. We anticipate that it can further evolve to achieve higher levels of automation for practical applications. Building upon SAM2, we conducted a series of practices that ultimately led to the development of a fully automated pipeline, termed Det-SAM2, in which object prompts are automatically generated by a detection model to facilitate inference and refinement by SAM2. This pipeline enables inference on infinitely long video streams with constant VRAM and RAM usage, all while preserving the same efficiency and accuracy as the original SAM2. This technical report focuses on the construction of the overall Det-SAM2 framework and the subsequent engineering optimization applied to SAM2. We present a case demonstrating an application built on the Det-SAM2 framework: AI refereeing in a billiards scenario, derived from our business context. The project at \url{https://github.com/motern88/Det-SAM2}. |
| title | Det-SAM2:Technical Report on the Self-Prompting Segmentation Framework Based on Segment Anything Model 2 |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.18977 |