| 000 | 05429nam a2200793 i 4500 | ||
|---|---|---|---|
| 001 | 7731573 | ||
| 003 | IEEE | ||
| 005 | 20200413152921.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 160513s2017 caua foab 000 0 eng d | ||
| 020 |
_z9781627059558 _qpaperback |
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| 020 |
_a9781627059435 _qebook |
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| 024 | 7 |
_a10.2200/S00726ED1V01Y201608COV009 _2doi |
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| 035 | _a(CaBNVSL)gtp00566484 | ||
| 035 | _a(OCoLC)962187663 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aQA76.9.Q36 _bB485 2017 |
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| 082 | 0 | 4 |
_a005.7 _223 |
| 100 | 1 |
_aBetke, Margrit, _eauthor. |
|
| 245 | 1 | 0 |
_aData association for multi-object visual tracking / _cMargrit Betke, Zheng Wu. |
| 264 | 1 |
_a[San Rafael, California] : _bMorgan & Claypool, _c2017. |
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| 300 |
_a1 PDF (ix, 110 pages) : _billustrations. |
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| 336 |
_astill image _2rdacontent |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 |
_aSynthesis lectures on computer vision, _x2153-1064 ; _v# 9 |
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| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 538 | _aMode of access: World Wide Web. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 504 | _aIncludes bibliographical references (pages 85-108). | ||
| 505 | 8 | _a8. Application to animal group tracking in 3D: 8.1. Two sample systems for analyzing bat and bird flight; 8.2. Impact of multi-animal tracking systems -- 9. Benchmarks for human tracking: 9.1. PETS-2009; 9.2. Beyond PETS-2009: the MOT-challenge benchmark -- 10. Concluding remarks -- Bibliography -- Authors' biographies. | |
| 505 | 0 | _aPreface -- 1. An introduction to data association in computer vision: 1.1. Challenges; 1.2. Related topics beyond the scope of this book; 1.3. Application domains; 1.4. Simulation testbeds; 1.5. Experimental benchmarks; 1.6. Organization of the book -- 2. Classic sequential data association approaches: 2.1. Advantages of Kalman filters for use in multi-object tracking; 2.2. Gating; 2.3. Global nearest neighbor standard filter (GNNSF); 2.4. Joint probabilistic data association (JPDA); 2.5. Multiple hypotheses tracking (MHT); 2.6. Discussion -- 3. Classic batch data association approaches: 3.1. Markov chain Monte Carlo data association (MCMCDA); 3.2. Network flow data association (NFDA); 3.3. Probabilistic multiple hypothesis tracking (PMHT); 3.4. Discussion -- 4. Evaluation criteria: 4.1. Definitions; 4.2. Discussion -- 5. Tracking with multiple cameras: 5.1. The reconstruction-tracking approach; 5.2. The tracking-reconstruction approach; 5.3. An example of spatial data association; 5.4. Discussion -- 6. The tracklet linking approach: 6.1. Review of existing work; 6.2. An example of tracklet linking using a track graph -- 7. Advanced techniques for data association: 7.1. Data association for merged or split measurements; 7.2. Learning-based data association; 7.3. Coupling data association -- | |
| 506 | 1 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | |
| 510 | 0 | _aCompendex | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 520 | 3 | _aThis 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. | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF title page (viewed on October 21, 2016). | ||
| 650 | 0 | _aData integration (Computer science) | |
| 650 | 0 |
_aComputer vision _xMathematical models. |
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| 650 | 0 |
_aAutomatic tracking _xMathematical models. |
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| 653 | _amulti-target tracking | ||
| 653 | _amulti-object tracking | ||
| 653 | _adata association | ||
| 653 | _amulti-view tracking | ||
| 653 | _amulti-camera tracking | ||
| 653 | _atracklet association | ||
| 653 | _atracklet linking | ||
| 653 | _atracklet stitching | ||
| 653 | _atracking evaluation | ||
| 653 | _aMOT evaluation | ||
| 653 | _aBayesian recursive filter | ||
| 653 | _aBayesian multi-target tracking | ||
| 653 | _aBayesian multi-object tracking | ||
| 653 | _apeople tracking | ||
| 653 | _aanimal tracking | ||
| 653 | _agroup tracking | ||
| 653 | _atracking bats | ||
| 653 | _atracking birds | ||
| 700 | 1 |
_aWu, Zheng, _eauthor. |
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| 776 | 0 | 8 |
_iPrint version: _z9781627059558 |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on computer vision ; _v# 9. _x2153-1064 |
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| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=7731573 |
| 999 |
_c562202 _d562202 |
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