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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Hyun, Eugin | - |
| dc.contributor.author | Jin, Youngseok | - |
| dc.date.accessioned | 2025-08-29T15:40:11Z | - |
| dc.date.available | 2025-08-29T15:40:11Z | - |
| dc.date.created | 2017-11-06 | - |
| dc.date.issued | 2017-07-19 | - |
| dc.identifier.isbn | 1601324642 | - |
| dc.identifier.uri | https://scholar.dgist.ac.kr/handle/20.500.11750/58974 | - |
| dc.description.abstract | We propose a multi-level fusion scheme for target detection using camera and radar sensors. For the proposed scheme, the radar sensor provides the target track information of the range, velocity, angle, and track ID. This data is applied during the vision processing step as the ROI (region of interest) where the target may exist. Next, for feature-level fusion, the Doppler spectrum of the ROI is provided to the sensor-fusion-based target classifier. In the classifier, we then determine the class of the target using an image database and a Doppler pattern database. In the experimental results, we verify the proposed processing scheme using a 24 GHz FMCW transceiver with a single antenna. CSREA Press © | - |
| dc.language | English | - |
| dc.publisher | CSREA Press | - |
| dc.relation.ispartof | Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017 | - |
| dc.title | Multi-level Fusion Scheme for Target Classification using Camera and Radar Sensors | - |
| dc.type | Conference Paper | - |
| dc.identifier.scopusid | 2-s2.0-85072917392 | - |
| dc.identifier.bibliographicCitation | 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017, pp.111 - 114 | - |
| dc.citation.conferenceDate | 2017-07-17 | - |
| dc.citation.conferencePlace | US | - |
| dc.citation.conferencePlace | Las Vegas | - |
| dc.citation.endPage | 114 | - |
| dc.citation.startPage | 111 | - |
| dc.citation.title | 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017 | - |