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Author Betke, Margrit.
Title Data association for multi-object visual tracking / Margrit Betke, Zheng Wu.
Publication Info [San Rafael, California] : Morgan & Claypool, 2017.



Descript 1 PDF (ix, 110 pages) : illustrations.
Note Part of: Synthesis digital library of engineering and computer science.
Contents 8. 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.
Preface -- 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 --
Note Abstract freely available; full-text restricted to subscribers or individual document purchasers.
System requirements: Adobe Acrobat Reader.
Mode of access: World Wide Web.
ISBN 9781627059558 paperback
9781627059435 ebook
Standard # 10.2200/S00726ED1V01Y201608COV009 doi
Click on the terms below to find similar items in the catalogue
Author Betke, Margrit.
Series Synthesis lectures on computer vision, # 9
Synthesis digital library of engineering and computer science.
Synthesis lectures on computer vision ; # 9. 2153-1064
Subject Data integration (Computer science)
Computer vision -- Mathematical models.
Automatic tracking -- Mathematical models.
Alt author Wu, Zheng.
Descript 1 PDF (ix, 110 pages) : illustrations.
Note Part of: Synthesis digital library of engineering and computer science.
Contents 8. 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.
Preface -- 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 --
Note Abstract freely available; full-text restricted to subscribers or individual document purchasers.
System requirements: Adobe Acrobat Reader.
Mode of access: World Wide Web.
ISBN 9781627059558 paperback
9781627059435 ebook
Standard # 10.2200/S00726ED1V01Y201608COV009 doi
Author Betke, Margrit.
Series Synthesis lectures on computer vision, # 9
Synthesis digital library of engineering and computer science.
Synthesis lectures on computer vision ; # 9. 2153-1064
Subject Data integration (Computer science)
Computer vision -- Mathematical models.
Automatic tracking -- Mathematical models.
Alt author Wu, Zheng.

Subject Data integration (Computer science)
Computer vision -- Mathematical models.
Automatic tracking -- Mathematical models.
Descript 1 PDF (ix, 110 pages) : illustrations.
Note Part of: Synthesis digital library of engineering and computer science.
Contents 8. 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.
Preface -- 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 --
Note Abstract freely available; full-text restricted to subscribers or individual document purchasers.
System requirements: Adobe Acrobat Reader.
Mode of access: World Wide Web.
Alt author Wu, Zheng.
ISBN 9781627059558 paperback
9781627059435 ebook
Standard # 10.2200/S00726ED1V01Y201608COV009 doi

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