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008			230509s2017 sz | s |||| 0|eng d
020			_a9783031018169
024	7		_a10.1007/978-3-031-01816-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.Q36 _b2017 EB
100	1		_aBetke, Margrit _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688452
245	1	0	_aData Association for Multi-Object Visual Tracking _cby Margrit Betke, Zheng Wu
250			_a1st edition 2017
264		1	_aCham _bSpringer International Publishing _c2017
300			_a1 recurso en línea (IX, 110 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Computer Vision _x2153-1064
505	0		_aPreface -- An Introduction to Data Association in Computer Vision -- Classic Sequential Data Association Approaches -- Classic Batch Data Association Approaches -- Evaluation Criteria -- Tracking with Multiple Cameras -- The Tracklet Linking Approach -- Advanced Techniques for Data Association -- Application to Animal Group Tracking in 3D -- Benchmarks for Human Tracking -- Concluding Remarks -- Bibliography -- Authors' Biographies .
520			_aIn the human quest for scientific knowledge, empirical evidence is collected by visual perception. Tracking with computer vision takes on the important role to reveal complex patterns of motion that exist in the world we live in. Multi-object tracking algorithms provide new information on how groups and individual group members move through three-dimensional space. They enable us to study in depth the relationships between individuals in moving groups. These may be interactions of pedestrians on a crowded sidewalk, living cells under a microscope, or bats emerging in large numbers from a cave. Being able to track pedestrians is important for urban planning; analysis of cell interactions supports research on biomaterial design; and the study of bat and bird flight can guide the engineering of aircraft. We were inspired by this multitude of applications to consider the crucial component needed to advance a single-object tracking system to a multi-object tracking system-data association. Data association in the most general sense is the process of matching information about newly observed objects with information that was previously observed about them. This information may be about their identities, positions, or trajectories. Algorithms for data association search for matches that optimize certain match criteria and are subject to physical conditions. They can therefore be formulated as solving a "constrained optimization problem"-the problem of optimizing an objective function of some variables in the presence of constraints on these variables. As such, data association methods have a strong mathematical grounding and are valuable general tools for computer vision researchers. This book serves as a tutorial on data association methods, intended for both students and experts in computer vision. We describe the basic research problems, review the current state of the art, and present some recently developed approaches. The book covers multi-object tracking in two and three dimensions. We consider two imaging scenarios involving either single cameras or multiple cameras with overlapping fields of view, and requiring across-time and across-view data association methods. In addition to methods that match new measurements to already established tracks, we describe methods that match trajectory segments, also called tracklets. The book presents a principled application of data association to solve two interesting tasks: first, analyzing the movements of groups of free-flying animals and second, reconstructing the movements of groups of pedestrians. We conclude by discussing exciting directions for future research.
988			_aSynthesis Collection of Technology_2017
650		7	_2embne _9159793 _aVisión por ordenador
650		7	_2embne _9495511 _aDatos masivos
700	1		_aWu, Zheng _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688453 _c(Computer scientist)
776	0	8	_iPrinted edition: _z9783031006883
776	0	8	_iPrinted edition: _z9783031029448
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01816-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eIG _zSI