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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Bibliographic Details
Main Authors: Verma, Brijesh, (Author), Zhang, Ligang, (Author), Stockwell, David, (Author)
Format: eBook
Language: English
Published: Singapore : Springer, 2017.
Series: Studies in computational intelligence ; v. 711.
Subjects:
Machine learning.
Data mining.
Digital video > Data processing.
elektronické knihy
electronic books
ISBN: 9789811045394
9789811045387
Physical Description: 1 online resource (xxv, 189 pages) : illustrations (some color)

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Table of contents

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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 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://link.springer.com/10.1007/978-981-10-4539-4  |y Plný text 
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