000			03449cam a2200469 i 4500
001			17966583
003			IIITD
005			20160830142116.0
008			131209s2014 enka b 001 0 eng c
010			_a 2013957125
020			_a9781447162957
035			_a(OCoLC)ocn864497923
040			_aBTCTA _beng _cBTCTA _erda _dYDXCP _dIXA _dOCLCF _dCDX _dDLC
042			_apcc
050	0	0	_aTK7882.P3 _bP468 2014
082			_a006.4 _bGON-P
100			_aGong, Shaogang
245	0	0	_aPerson re-identification _cShaogang Gong, Marco Cristani, Shuicheng Yan, Chen Change Loy, editors.
260			_aLondon : _bSpringer, _c©2014.
300			_axviii, 445 p. : _bill.; _c25 cm.
504			_aIncludes bibliographical references and index.
520			_aRe-identification offers a useful tool for non-invasive biometric validation, surveillance, and human-robot interaction in a broad range of applications from crowd traffic management to personalised healthcare. This comprehensive volume is the first work of its kind dedicated to addressing the challenge of Person Re-Identification, presenting insights from an international selection of leading authorities in the field. Taking a strongly multidisciplinary approach, the text provides an in-depth discussion of recent developments and state-of-the-art methods drawn from the computer vision, pattern recognition and machine learning communities, embracing both fundamental research and practical applications. Topics and features: Introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms, and examines the benefits of semantic attributes. Describes how to segregate meaningful body parts from background clutter. Examines the use of 3D depth images, and contextual constraints derived from the visual appearance of a group. Reviews approaches to feature transfer function and distance metric learning, and discusses potential solutions to issues of data scalability and identity inference. Investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference, and describes techniques for improving post-rank search efficiency. Explores the design rationale and implementation considerations of building a practical re-identification system. This timely collection will be of great interest to academics, industrial researchers and postgraduates involved in computer vision and machine learning, database image retrieval, big data mining, and search engines, as well as to developers keen to exploit this emerging technology for commercial applications. --
650		0	_aPattern recognition systems.
650		0	_aComputer vision.
650		0	_aVideo surveillance.
650		0	_aBiometric identification.
650		0	_aDigital video.
650		7	_aBiometric identification. _2fast
650		7	_aComputer vision. _2fast
650		7	_aDigital video. _2fast
650		7	_aPattern recognition systems. _2fast
650		7	_aVideo surveillance. _2fast
700	1		_aGong, Shaogang, _eeditor.
700	1		_aCristani, Marco, _eeditor.
700	1		_aYan, Shuicheng, _eeditor.
700	1		_aLoy, Chen Change, _eeditor.
830		0	_aAdvances in computer vision and pattern recognition,
906			_a7 _bcbc _cpccadap _d2 _eepcn _f20 _gy-gencatlg
942			_2ddc _cBK
999			_c13060 _d13060