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Deep Understanding of Urban Mobility from CityscapeWebcams
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Deep Understanding of Urban Mobility from CityscapeWebcams. |
| 개인저자 | Zhang, Shanghang. |
| 단체저자명 | Carnegie Mellon University. Electrical and Computer Engineering. |
| 발행사항 | [S.l.]: Carnegie Mellon University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 129 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-09B(E). Dissertation Abstract International |
| ISBN | 9780355958812 |
| 학위논문주기 | Thesis (Ph.D.)--Carnegie Mellon University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Advisers: Jose MF Moura |
| 이용제한사항 | This item is not available from ProQuest Dissertations & Theses. |
| 요약 | Deep understanding of urban mobility is of great significance for many real-world applications, such as urban traffic management and autonomous driving. This thesis develops deep learning methodologies to extract vehicle counts from streaming re |
| 일반주제명 | Computer engineering. Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
