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Human Activity Recognition and Prediction
This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discuss...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2016.
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| Edición: | 1st ed. 2016. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 024 | 7 | |a 10.1007/978-3-319-27004-3 |2 doi | |
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| 245 | 1 | 0 | |a Human Activity Recognition and Prediction |h [electronic resource] / |c edited by Yun Fu. |
| 250 | |a 1st ed. 2016. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2016. | |
| 300 | |a VII, 174 p. 70 illus., 64 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 505 | 0 | |a Introduction -- Action and Activities -- Action Recognition and Human Interaction -- Multimodal Action Recognition -- RGB-D Action Recognition -- Actionlets and Activity Prediction -- Time Series Modeling for Activity Prediction -- RGB-D Action Prediction. | |
| 520 | |a This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques. . | ||
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Biometric identification. | |
| 650 | 1 | 4 | |a Signal, Speech and Image Processing . |
| 650 | 2 | 4 | |a Computer Vision. |
| 650 | 2 | 4 | |a Biometrics. |
| 700 | 1 | |a Fu, Yun. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319270029 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319270036 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319800554 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-27004-3 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||