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008    160513s2017    caua   fsab   000 0 eng d 
020    |z9781627059558|qpaperback 
020    9781627059435|qebook 
024 7  10.2200/S00726ED1V01Y201608COV009|2doi 
035    (WaSeSS)ssj0002056026 
040    CaBNVSL|beng|cCaBNVSL|dCaBNVSL|dWaSeSS 
050  4 QA76.9.Q36|bB485 2017 
082 04 005.7|223 
100 1  Betke, Margrit. 
245 10 Data association for multi-object visual tracking /
       |cMargrit Betke, Zheng Wu. 
260    [San Rafael, California] :|bMorgan & Claypool,|c2017. 
300    1 PDF (ix, 110 pages) :|billustrations. 
490 1  Synthesis lectures on computer vision,|v# 9 
500    Part of: Synthesis digital library of engineering and 
       computer science. 
505 8  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. 
505 0  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 -- 
506 1  Abstract freely available; full-text restricted to 
       subscribers or individual document purchasers. 
538    System requirements: Adobe Acrobat Reader. 
538    Mode of access: World Wide Web. 
650  0 Data integration (Computer science) 
650  0 Computer vision|xMathematical models. 
650  0 Automatic tracking|xMathematical models. 
700 1  Wu, Zheng. 
830  0 Synthesis digital library of engineering and computer 
       science. 
830  0 Synthesis lectures on computer vision ;|v# 9.|x2153-1064 
856 40 |uhttps://ieeexplore.ieee.org/servlet/
       opac?bknumber=7731573|zFull text available from IEEE 
       Xplore Morgan & Claypool Synthesis eBooks Library Computer
       & Information Science Collection Eight 
921    .