Roadside video data analysis : deep learning
This book highlights the methods and applications for roadside video data analysis, with a particular focus on the use of deep learning to solve roadside video data segmentation and classification problems. It describes system architectures and methodologies that are specifically built upon learning...
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| Main Authors | , , |
|---|---|
| Format | Electronic eBook |
| Language | English |
| Published |
Singapore :
Springer,
2017.
|
| Series | Studies in computational intelligence ;
v. 711. |
| Subjects | |
| Online Access | Full text |
| ISBN | 9789811045394 9789811045387 |
| ISSN | 1860-949X ; |
| Physical Description | 1 online resource (xxv, 189 pages) : illustrations (some color) |
Cover
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| 100 | 1 | |a Verma, Brijesh, |e author. | |
| 245 | 1 | 0 | |a Roadside video data analysis : |b deep learning / |c Brijesh Verma, Ligang Zhang, David Stockwell. |
| 264 | 1 | |a Singapore : |b Springer, |c 2017. | |
| 300 | |a 1 online resource (xxv, 189 pages) : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a počítač |b c |2 rdamedia | ||
| 338 | |a online zdroj |b cr |2 rdacarrier | ||
| 490 | 1 | |a Studies in computational intelligence, |x 1860-949X ; |v volume 711 | |
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a Chapter 1: Introduction -- Chapter 2: Roadside Video Data Analysis Framework -- Chapter 3: Non-Deep Learning Techniques for Roadside Video Data Analysis -- Chapter 4: Deep Learning Techniques for Roadside Video Data Analysis -- Chapter 5: Case Study: Roadside Video Data Analysis for Fire Risk Assessment -- Chapter 6: Conclusion and Future Insight -- References. | |
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty | ||
| 520 | |a This book highlights the methods and applications for roadside video data analysis, with a particular focus on the use of deep learning to solve roadside video data segmentation and classification problems. It describes system architectures and methodologies that are specifically built upon learning concepts for roadside video data processing, and offers a detailed analysis of the segmentation, feature extraction and classification processes. Lastly, it demonstrates the applications of roadside video data analysis including scene labelling, roadside vegetation classification and vegetation biomass estimation in fire risk assessment. | ||
| 590 | |a SpringerLink |b Springer Complete eBooks | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Digital video |x Data processing. | |
| 655 | 7 | |a elektronické knihy |7 fd186907 |2 czenas | |
| 655 | 9 | |a electronic books |2 eczenas | |
| 700 | 1 | |a Zhang, Ligang, |e author. | |
| 700 | 1 | |a Stockwell, David, |e author. | |
| 776 | 0 | 8 | |i Print version: |a Verma, Brijesh. |t Roadside video data analysis. |d Singapore : Springer, 2017 |z 9789811045387 |z 9811045380 |w (OCoLC)978290369 |
| 830 | 0 | |a Studies in computational intelligence ; |v v. 711. |x 1860-949X | |
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