Self-Supervised Terrain Classification Based on Moving Objects Using Monoculr Camera
- Title
- Self-Supervised Terrain Classification Based on Moving Objects Using Monoculr Camera
- Author
- 서일홍
- Keywords
- Roads; Robots; Humans; Image color analysis; Data mining; Vehicles; Feature extraction
- Issue Date
- 2011-12
- Publisher
- IEEE
- Citation
- Institute of Electrical and Electronics Engineers, 2011
- Abstract
- For autonomous robots equipped with a camera, terrain classification is essential in finding a safe pathway to a destination. Terrain classification is based on learning, but the amount of data cannot be infinite. This paper presents a self-supervised classification approach to enable a robot to learn the visual appearance of terrain classes in various outdoor environments by observing moving objects, such as humans and vehicles, and to learn about the terrain, based on their paths of movement. We verified the performance of our proposed method experimentally and compared the results with those obtained using supervised classification. The difference in error rates between self-supervised and supervised methods was about 0–11%.
- URI
- https://ieeexplore.ieee.org/abstract/document/6181340/https://repository.hanyang.ac.kr/handle/20.500.11754/70548
- ISBN
- 9781457721380; 9781457721366; 9781457721373
- DOI
- 10.1109/ROBIO.2011.6181340
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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