000			04385nam a22004455i 4500
999			_c387430 _d387430
001			387430
003			ES-MaUEC
005			20230330091142.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			230330s2013 sz | s |||| 0|eng d
020			_a9783031022494
024	7		_a10.1007/978-3-031-02249-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK6680.3 _b2013 EB
100	1		_aThida, Myo _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687905
245	1	0	_aContextual Analysis of Videos _cby Myo Thida, How-lung Eng, Dorothy Monekosso, Paolo Remagnino
250			_a1st edition 2013
264		1	_aCham _bSpringer International Publishing _c2013
300			_a1 recurso en línea (XCVI, 8 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 -- Literature Review -- Tracking Multiple Targets Using Particle Swarm Optimization -- Abnormality Detection in Crowded Scenes -- Conclusion -- Bibliography -- Authors' Biographies.
520			_aVideo context analysis is an active and vibrant research area, which provides means for extracting, analyzing and understanding behavior of a single target and multiple targets. Over the last few decades, computer vision researchers have been working to improve the accuracy and robustness of algorithms to analyse the context of a video automatically. In general, the research work in this area can be categorized into three major topics: 1) counting number of people in the scene 2) tracking individuals in a crowd and 3) understanding behavior of a single target or multiple targets in the scene. This book focusses on tracking individual targets and detecting abnormal behavior of a crowd in a complex scene. Firstly, this book surveys the state-of-the-art methods for tracking multiple targets in a complex scene and describes the authors' approach for tracking multiple targets. The proposed approach is to formulate the problem of multi-target tracking as an optimization problem of finding dynamic optima (pedestrians) where these optima interact frequently. A novel particle swarm optimization (PSO) algorithm that uses a set of multiple swarms is presented. Through particles and swarms diversification, motion prediction is introduced into the standard PSO, constraining swarm members to the most likely region in the search space. The social interaction among swarm and the output from pedestrians-detector are also incorporated into the velocity-updating equation. This allows the proposed approach to track multiple targets in a crowded scene with severe occlusion and heavy interactions among targets. The second part of this book discusses the problem of detecting and localising abnormal activities in crowded scenes. We present a spatio-temporal Laplacian Eigenmap method for extracting different crowd activities from videos. This method learns the spatial and temporal variations of local motions in an embedded space and employs representatives of different activities to construct the model which characterises the regular behavior of a crowd. This model of regular crowd behavior allows for the detection of abnormal crowd activities both in local and global context and the localization of regions which show abnormal behavior.
988			_aSynthesis Collection of Technology_2013
650		7	_2embne _9156182 _aVídeo digital
650		7	_2embne _9159793 _aVisión por ordenador _xModelos matemáticos
700	1		_aEng, How-lung _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687906
700	1		_aMonekosso, Dorothy _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687907
700	1		_aRemagnino, Paolo, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9677293 _d1963-
776	0	8	_iPrinted edition: _z9783031011214
776	0	8	_iPrinted edition: _z9783031033773
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02249-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eIG _zSI