Princeton University Library Catalog

Data association for multi-object visual tracking / Margrit Betke, Zheng Wu.

Betke, Margrit [Browse]
[San Rafael, California] : Morgan & Claypool, 2017.
1 online resource (ix, 110 pages) : illustrations.
Summary note:
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.
Part of: Synthesis digital library of engineering and computer science.
Bibliographic references:
Includes bibliographical references (pages 85-108).
Source of description:
Title from PDF title page (viewed on October 21, 2016).
  • 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 --
  • 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.
Indexed in:
  • Compendex
  • Google scholar
  • Google book search
  • 9781627059435 (ebook)
  • (paperback)
  • 10.2200/S00726ED1V01Y201608COV009
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