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Roadside Video Data Analysis [electronic resource] : Deep Learning / by Brijesh Verma, Ligang Zhang, David Stockwell.

By: Verma, Brijesh [author.].
Contributor(s): Zhang, Ligang [author.] | Stockwell, David [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 711Publisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: XXV, 189 p. 79 illus., 68 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789811045394.Subject(s): Signal processing | Computational intelligence | User interfaces (Computer systems) | Human-computer interaction | Image processing—Digital techniques | Computer vision | Transportation engineering | Traffic engineering | Signal, Speech and Image Processing | Computational Intelligence | User Interfaces and Human Computer Interaction | Computer Imaging, Vision, Pattern Recognition and Graphics | Transportation Technology and Traffic EngineeringAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
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.
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.
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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.

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