000			04289nam a22004455i 4500
999			_c387422 _d387422
001			387422
003			ES-MaUEC
005			20230321173515.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220601s2005 sz | s |||| 0|eng d
020			_a9783031022364
024	7		_a10.1007/978-3-031-02236-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7882.B56 _b2005 EB
100	1		_aChellappa, Rama _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687507
245	1	0	_aRecognition of Humans and Their Activities Using Video _cby Rama Chellappa, Amit K. Roy-Chowdhury, S. Kevin Zhou
250			_a1st edition 2005
264		1	_aCham _bSpringer International Publishing _c2005
300			_a1 recurso en línea (IX, 171 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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 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.
988			_aSynthesis Collection of Technology_2005
650		7	_2embne _9687509 _aIdentificación biométrica
650		7	_2embne _9145027 _aLocomoción
650		7	_2embne _9681178 _aReconocimiento facial (Informática)
700	1		_aRoy-Chowdhury, Amit K. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686990
700	1		_aZhou, S. Kevin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687508
776	0	8	_iPrinted edition: _z9783031011085
776	0	8	_iPrinted edition: _z9783031033643
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02236-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI