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024 7 10.1007/978-3-319-04561-0|2doi
072 7 UYT|2bicssc
072 7 UYQV|2bicssc
072 7 COM012000|2bisacsh
072 7 COM016000|2bisacsh
082 04 006.6|223
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100 1 Wang, Jiang.|eauthor.
245 10 Human Action Recognition with Depth Cameras|h[electronic
resource] /|cby Jiang Wang, Zicheng Liu, Ying Wu.
264 1 Cham :|bSpringer International Publishing :|bImprint:
300 VIII, 59 p. 32 illus., 9 illus. in color.|bonline
338 online resource|bcr|2rdacarrier
347 text file|bPDF|2rda
490 1 SpringerBriefs in Computer Science,|x2191-5768
490 0 Springer eBooks.|aComputer Science
505 0 Introduction -- Learning Actionlet Ensemble for 3D Human
Action Recognition -- Random Occupancy Patterns --
520 Action recognition is an enabling technology for many real
world applications, such as human-computer interaction,
surveillance, video retrieval, retirement home monitoring,
and robotics. In the past decade, it has attracted a great
amount of interest in the research community. Recently,
the commoditization of depth sensors has generated much
excitement in action recognition from depth sensors. New
depth sensor technology has enabled many applications that
were not feasible before. On one hand, action recognition
becomes far easier with depth sensors. On the other hand,
the drive to recognize more complex actions presents new
challenges. One crucial aspect of action recognition is to
extract discriminative features. The depth maps have
completely different characteristics from the RGB images.
Directly applying features designed for RGB images does
not work. Complex actions usually involve complicated
temporal structures, human-object interactions, and person
-person contacts. New machine learning algorithms need to
be developed to learn these complex structures. This work
enables the reader to quickly familiarize themselves with
the latest research in depth-sensor based action
recognition, and to gain a deeper understanding of
recently developed techniques. It will be of great use for
both researchers and practitioners who are interested in
human action recognition with depth sensors. The text
focuses on feature representation and machine learning
algorithms for action recognition from depth sensors.
After presenting a comprehensive overview of the state of
the art in action recognition from depth data, the authors
then provide in-depth descriptions of their recently
developed feature representations and machine learning
techniques, including lower-level depth and skeleton
features, higher-level representations to model the
temporal structure and human-object interactions, and
feature selection techniques for occlusion handling.
650 0 Computer science.
650 0 User interfaces (Computer systems).
650 0 Image processing.
650 0 Biometrics (Biology).
650 14 Computer Science.
650 24 Image Processing and Computer Vision.
650 24 Biometrics.
650 24 User Interfaces and Human Computer Interaction.
700 1 Liu, Zicheng.,|eauthor.
700 1 Wu, Ying.,|eauthor.
710 2 SpringerLink (Online service)
773 0 |tSpringer eBooks
776 08 |iPrinted edition:|z9783319045603
830 0 SpringerBriefs in Computer Science,|x2191-5768
856 40 |uhttps://cuarzo.unizar.es:9443/login?url=https://
dx.doi.org/10.1007/978-3-319-04561-0|zAcceso al texto
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