000 04995nam a2200649 i 4500
001 6812668
003 IEEE
005 20200413152849.0
006 m eo d
007 cr bn |||m|||a
008 081006s2005 cau fob 000 0 eng d
020 _a1598290061 (electronic bk.)
020 _a9781598290066 (electronic bk.)
024 7 _a10.2200/S00002ED1V01Y200508IVM001
_2doi
035 _a(OCoLC)62412967
035 _a(CaBNVSL)gtp00531489
040 _aCaBNVSL
_cCaBNVSL
_dCaBNVSL
050 4 _aTK7882.B56
_bC54 2005
082 0 4 _a006.4
_222
090 _a
_bMoCl
_e200508IVM001
100 1 _aChellappa, Rama.
245 1 0 _aRecognition of humans and their activities using video
_h[electronic resource] /
_cRama Chellappa, Amit K. Roy-Chowdhury, S. Kevin Zhou.
250 _a1st ed.
260 _aSan Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) :
_bMorgan & Claypool Publishers,
_cc2005.
300 _a1 electronic document (ix, 173 p.) :
_bdigital file.
490 1 _aSynthesis lectures on image, video, and multimedia processing ;
_v#1
538 _aMode of access: World Wide Web.
538 _aSystem requirements: PDF reader.
500 _aPart of: Synthesis digital library of engineering and computer science.
500 _aSeries from website.
500 _aSeries statement from caption on home page.
504 _aIncludes bibliographical references (p. 153-170).
505 0 _aIntroduction -- Human recognition using face -- Human recognition using gait -- Human activity recognition -- Future research directions -- Conclusions -- References.
506 1 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
506 _aAvailable to subscribers only.
510 0 _aCompendex
510 0 _aGoogle book search
510 0 _aGoogle scholar
510 0 _aINSPEC
520 0 _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 fusion of 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.
588 _aTitle from PDF t.p. (viewed on Oct. 10, 2008).
650 0 _aBiometric identification.
650 0 _aGait in humans.
650 0 _aHuman face recognition (Computer science)
650 0 _aImage analysis.
650 0 _aImage processing
_xDigital techniques.
690 _aPattern recognition.
690 _aFace recognition.
690 _aGait recognition.
690 _aHuman activity recognition.
700 1 _aRoy-Chowdhury, Amit K.
700 1 _aZhou, S. Kevin.
730 0 _aSynthesis digital library of engineering and computer science.
830 0 _aSynthesis lectures on image, video, and multimedia processing ;
_v#1.
856 4 2 _3Abstract with links to resource
_uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6812668
856 4 0 _3Abstract with links to full text
_uhttp://dx.doi.org/10.2200/S00002ED1V01Y200508IVM001
999 _c561578
_d561578