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020 _a9783031022364
_9978-3-031-02236-4
024 7 _a10.1007/978-3-031-02236-4
_2doi
050 4 _aT1-995
072 7 _aTBC
_2bicssc
072 7 _aTEC000000
_2bisacsh
072 7 _aTBC
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082 0 4 _a620
_223
100 1 _aChellappa, Rama.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_986982
245 1 0 _aRecognition of Humans and Their Activities Using Video
_h[electronic resource] /
_cby Rama Chellappa, Amit K. Roy-Chowdhury, S. Kevin Zhou.
250 _a1st ed. 2005.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2005.
300 _aIX, 171 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Image, Video, and Multimedia Processing,
_x1559-8144
505 0 _aIntroduction -- Human Recognition Using Face -- Human Recognition Using Gait -- Human Activity Recognition -- Future Research Directions -- Conclusions.
520 _aThe recognition of humans and their activities from video sequences is currently a very active area of research because of its applications in video surveillance, design of realistic entertainment systems, multimedia communications, and medical diagnosis. In this lecture, we discuss the use of face and gait signatures for human identification and recognition of human activities from video sequences. We survey existing work and describe some of the more well-known methods in these areas. We also describe our own research and outline future possibilities. In the area of face recognition, we start with the traditional methods for image-based analysis and then describe some of the more recent developments related to the use of video sequences, 3D models, and techniques for representing variations of illumination. We note that the main challenge facing researchers in this area is the development of recognition strategies that are robust to changes due to pose, illumination, disguise, and aging. Gait recognition is a more recent area of research in video understanding, although it has been studied for a long time in psychophysics and kinesiology. The goal for video scientists working in this area is to automatically extract the parameters for representation of human gait. We describe some of the techniques that have been developed for this purpose, most of which are appearance based. We also highlight the challenges involved in dealing with changes in viewpoint and propose methods based on image synthesis, visual hull, and 3D models. In the domain of human activity recognition, we present an extensive survey of various methods that have been developed in different disciplines like artificial intelligence, image processing, pattern recognition, and computer vision. We then outline our method for modeling complex activities using 2D and 3D deformable shape theory. The wide application of automatic human identification and activity recognition methods will require the fusionof different modalities like face and gait, dealing with the problems of pose and illumination variations, and accurate computation of 3D models. The last chapter of this lecture deals with these areas of future research.
650 0 _aEngineering.
_99405
650 0 _aElectrical engineering.
_986984
650 0 _aSignal processing.
_94052
650 1 4 _aTechnology and Engineering.
_986986
650 2 4 _aElectrical and Electronic Engineering.
_986987
650 2 4 _aSignal, Speech and Image Processing.
_931566
700 1 _aRoy-Chowdhury, Amit K.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_986988
700 1 _aZhou, S. Kevin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_986989
710 2 _aSpringerLink (Online service)
_986993
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031011085
776 0 8 _iPrinted edition:
_z9783031033643
830 0 _aSynthesis Lectures on Image, Video, and Multimedia Processing,
_x1559-8144
_986995
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02236-4
912 _aZDB-2-SXSC
942 _cEBK
999 _c86034
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