![Roadside video data analysis [electronic resource] : deep learning](https://libdoc.korea.ac.kr/dl_image/IMG/14/000000348792/INTROIMAGE/000000348792_01.JPG)
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| 008 | 190726s2017 si a ob 000 0 eng d | |
| 020 | ▼a 9789811045387 | |
| 020 | ▼a 9789811045394 (eBook) | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
| 050 | 4 | ▼a TK5102.9 |
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| 084 | ▼a 006.31 ▼2 DDCK | |
| 090 | ▼a 006.31 | |
| 100 | 1 | ▼a Verma, Brijesh. |
| 245 | 1 0 | ▼a Roadside video data analysis ▼h [electronic resource] : ▼b deep learning / ▼c Brijesh Verma, Ligang Zhang, David Stockwell. |
| 260 | ▼a Singapore : ▼b Springer, ▼c 2017. | |
| 300 | ▼a 1 online resource (xxv, 189 p.) : ▼b ill. (some col.). | |
| 490 | 1 | ▼a Studies in computational intelligence, ▼x 1860-949X ; ▼v 711 |
| 500 | ▼a Title from e-Book title page. | |
| 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. |
| 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. | |
| 530 | ▼a Issued also as a book. | |
| 538 | ▼a Mode of access: World Wide Web. | |
| 650 | 0 | ▼a Machine learning. |
| 650 | 0 | ▼a Data mining. |
| 650 | 0 | ▼a Digital video ▼x Data processing. |
| 700 | 1 | ▼a Zhang, Ligang. |
| 700 | 1 | ▼a Stockwell, David. |
| 830 | 0 | ▼a Studies in computational intelligence ; ▼v 711. |
| 856 | 4 0 | ▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-981-10-4539-4 |
| 945 | ▼a KLPA | |
| 991 | ▼a E-Book(소장) |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/e-Book 컬렉션/ | 청구기호 CR 006.31 | 등록번호 E14016107 | 도서상태 대출불가(열람가능) | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
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.
New feature
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.정보제공 :
목차
1 Introduction Background Collection of Roadside Video Data Industry Data Benchmark Data Applications Using Roadside Video Data Outline of the Book 2 Roadside Video Data Analysis Framework Overview Methodology Preprocessing of Roadside Video Data Segmentation of Roadside Video Data into Objects Vegetation, Roads, Signs, Sky Feature Extraction from Objects Classification of Roadside Objects Applications of Classified Roadside Objects Algorithms and Pseudocodes 3 Learning and Impact on Roadside Video Data Analysis Neural Network Learning Support Vector Machine Learning K-Nearest Neighbor Learning Cluster Learning Hierarchical Learning Fuzzy C-Means Learning Region Merging Learning Probabilistic Learning Ensemble Learning Deep Learning 4 Applications in Roadside Fire Risk Assessment Scene Labeling Roadside Vegetation Classification Vegetation Biomass Estimation 5 Conclusions and Future Insights Recommendations New Challenges New Opportunities and Applications
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