| LDR | | 01708nam u200409 4500 |
| 001 | | 000000417984 |
| 005 | | 20190215162453 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780355958812 |
| 035 | |
▼a (MiAaPQ)AAI10822878 |
| 035 | |
▼a (MiAaPQ)cmu:10258 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Zhang, Shanghang. |
| 245 | 10 |
▼a Deep Understanding of Urban Mobility from CityscapeWebcams. |
| 260 | |
▼a [S.l.]:
▼b Carnegie Mellon University.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 129 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B. |
| 500 | |
▼a Advisers: Jose MF Moura |
| 502 | 1 |
▼a Thesis (Ph.D.)--Carnegie Mellon University, 2018. |
| 506 | |
▼a This item is not available from ProQuest Dissertations & Theses. |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0041. |
| 650 | 4 |
▼a Computer engineering. |
| 650 | 4 |
▼a Artificial intelligence. |
| 690 | |
▼a 0464 |
| 690 | |
▼a 0800 |
| 710 | 20 |
▼a Carnegie Mellon University.
▼b Electrical and Computer Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-09B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0041 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998516
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |