AAAI 2025technical0 citations
SDAS: Semantic Data Acquisition System for Minimizing Redundancy and Maximizing Diversity
Yeseung Park, Hyunse Yoon, Jungwoo Huh, Jungsu Kim, Jeongwook Choi, Sanghoon Lee
Abstract
In this paper, we propose SDAS, a new motion data assessment and storage system designed to acquire new motion data with reduced redundancy and maximizing diversity. SDAS collects data in the field, retrieves the most similar data from the database in real-time, and provides visualization tools that allow for the comparison of differences between the capture data and the stored data. Through this system, researchers can efficiently build and manage a database. The demonstration video is available at https://youtu.be/vqW0uMDnZTw.
BibTeX
@article{Park_Yoon_Huh_Kim_Choi_Lee_2025, title={SDAS: Semantic Data Acquisition System for Minimizing Redundancy and Maximizing Diversity}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35365}, DOI={10.1609/aaai.v39i28.35365}, abstractNote={In this paper, we propose SDAS, a new motion data assessment and storage system designed to acquire new motion data with reduced redundancy and maximizing diversity. SDAS collects data in the field, retrieves the most similar data from the database in real-time, and provides visualization tools that allow for the comparison of differences between the capture data and the stored data. Through this system, researchers can efficiently build and manage a database. The demonstration video is available at https://youtu.be/vqW0uMDnZTw.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Park, Yeseung and Yoon, Hyunse and Huh, Jungwoo and Kim, Jungsu and Choi, Jeongwook and Lee, Sanghoon}, year={2025}, month={Apr.}, pages={29679-29681} }