000			04231cam a22004451a 4500
008			100615s2005 cau fsb 000 0 eng d
020			_a1598290061 (electronic bk.)
020			_a9781598290066 (electronic bk.)
035			_a(WaSeSS)ssj0000328591
082	0	4	_a006.4CHE-R _222
100	1		_aChellappa, Rama.
245	1	0	_aRecognition of humans and their activities using video _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.
490	1		_aSynthesis lectures on image, video, and multimedia processing ; _v#1
500			_aPart of: Synthesis digital library of engineering and computer science.
500			_aSeries from website.
500			_aSeries statement from caption on home page.
500			_aTitle from PDF t.p. (viewed on Oct. 10, 2008).
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.
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.
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.
700	1		_aRoy-Chowdhury, Amit K.
700	1		_aZhou, S. Kevin.
856	4	0	_uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio7818000
942			_2ddc _cBK
210	1	0	_aRecognition of humans and their activities using video
506			_aLicense restrictions may limit access.
538			_aMode of access: World Wide Web.
538			_aSystem requirements: PDF reader.
730	0		_aSynthesis digital library of engineering and computer science.
773		0	_tSynthesis Collection One
830		0	_aSynthesis lectures on image, video, and multimedia processing ; _v#1.
999			_c22059 _d22059