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001 978-3-031-01816-9
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007 cr nn 008mamaa
008 220601s2017 sz | s |||| 0|eng d
020 _a9783031018169
_9978-3-031-01816-9
024 7 _a10.1007/978-3-031-01816-9
_2doi
050 4 _aTA1501-1820
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYT
_2thema
082 0 4 _a006
_223
100 1 _aBetke, Margrit.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983165
245 1 0 _aData Association for Multi-Object Visual Tracking
_h[electronic resource] /
_cby Margrit Betke, Zheng Wu.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aIX, 110 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_983167
650 0 _aPattern recognition systems.
_93953
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aComputer Vision.
_983173
650 2 4 _aAutomated Pattern Recognition.
_931568
700 1 _aWu, Zheng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983174
710 2 _aSpringerLink (Online service)
_983177
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031006883
776 0 8 _iPrinted edition:
_z9783031029448
830 0 _aSynthesis Lectures on Computer Vision,
_x2153-1064
_983178
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01816-9
912 _aZDB-2-SXSC
942 _cEBK
999 _c85463
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