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Roadside video data analysisdeep learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Roadside video data analysisby Brijesh Verma, Ligang Zhang, David Stockwell.
Reminder of title:	deep learning /
Author:	Verma, Brijesh.
other author:	Zhang, Ligang.
Published:	Singapore :Springer Singapore :2017.
Description:	xxv, 189 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	http://dx.doi.org/10.1007/978-981-10-4539-4
ISBN:	9789811045394$q(electronic bk.)

Roadside video data analysisdeep learning /
Verma, Brijesh.

Roadside video data analysisdeep learning /[electronic resource] :by Brijesh Verma, Ligang Zhang, David Stockwell. - Singapore :Springer Singapore :2017. - xxv, 189 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.7111860-949X ;. - Studies in computational intelligence ;v. 216..

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.

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.

ISBN: 9789811045394$q(electronic bk.)

Standard No.: 10.1007/978-981-10-4539-4doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

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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.
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.
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