Roadside Video Data Analysis [electronic resource] : Deep Learning /

By:

Verma, Brijesh [author.]

Contributor(s):

Language: ENG Series: Studies in Computational Intelligence ; 711Publisher: Singapore : Springer Singapore : Imprint: Springer, 2017Edition: 1st ed. 2017Description: XXV, 189 p. 79 illus., 68 illus. in color. | Binding - Card Paper |Content type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9789811045394

Subject(s):

Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:

621.382 23

Online resources:

Click here to access eBook in Springer Nature platform. (Within Campus only.)

In: Springer Nature eBookSummary: 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.

List(s) this item appears in: Springer Nature eBooks

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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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