高雄醫學大學圖書館 |

語系/ Language: 繁體中文

登入

回首頁 到查詢結果 [ subject:"Computer vision" ]

Data association for multi-object vi...

Betke, Margrit,

Data association for multi-object visual tracking /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Data association for multi-object visual tracking // Margrit Betke, Boston University, Zheng Wu, The Mathworks, Inc
作者:	Betke, Margrit,
其他作者:	Wu, Zheng
面頁冊數:	1 online resource (ix, 110 pages) :illustrations :
提要註:	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
標題:	Data integration (Computer science) -
電子資源:	http://portal.igpublish.com/iglibrary/search/MCPB0006279.html
ISBN:	9781627059435

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

Data association for multi-object visual tracking /Margrit Betke, Boston University, Zheng Wu, The Mathworks, Inc - 1 online resource (ix, 110 pages) :illustrations - Synthesis lectures on computer vision,#92153-1064 ;. - Synthesis lectures on computer vision ;#9..

Includes bibliographical references (pages 85-108)

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

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

ISBN: 9781627059435

Standard No.: 10.2200 / S00726ED1V01Y201608COV009doiSubjects--Topical Terms:

437843
Data integration (Computer science)
Subjects--Index Terms:

multi-object trackingIndex Terms--Genre/Form:

382946
Electronic books

LC Class. No.: QA76.9.Q36 / B485 2017

Dewey Class. No.: 005.7

Data association for multi-object visual tracking /
LDR:04719nam0a2200565 ib450 001 316294
005 20181023224029.0
006 m o d
007 cr cnu---unuuu
008 181207t20172017caua fob 000 0 eng d
020 $a 9781627059435 $q (ebook)
020 $a 1627059431 $q (ebook)
020 $z 9781627059558 $q (paperback)
024 7 $a 10.2200 / S00726ED1V01Y201608COV009 $2 doi
035 $a IGP290282
040 $a YDX $b eng $e rda $e pn $c YDX $d OCLCF $d J2I $d WAU $d EBLCP $d UIU $d $d COO $d NTBC
050 4 $a QA76.9.Q36 $b B485 2017
082 0 4 $a 005.7 $2 23
100 1 $a Betke, Margrit, $e author $3 437838
245 1 0 $a Data association for multi-object visual tracking / $c Margrit Betke, Boston University, Zheng Wu, The Mathworks, Inc
264 1 $a [San Rafael, California] : $b Morgan & Claypool Publishers, $c [2017]
264 4 $c c2017
300 $a 1 online resource (ix, 110 pages) : $b illustrations
336 $a text $b txt $2 rdacontent
337 $a computer $2 rdamedia
338 $a online resource $2 rdacarrier
490 0 $a Synthesis lectures on computer vision, $x 2153-1064 ; $v #9
504 $a Includes bibliographical references (pages 85-108)
505 0 $a 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
505 0 0 $a 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
520 $a 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
588 $a Online resource; title from PDF title page (Morgan & Claypool, viewed on October 21, 2016)
650 0 $a Data integration (Computer science) $3 437843
650 0 $a Computer vision $x Mathematical models $3 437842
650 0 $a Automatic tracking $x Mathematical models $3 437841
653 $a multi-object tracking
653 $a multi-target tracking
653 $a data association
653 $a multi-view tracking
653 $a multi-camera tracking
653 $a tracklet association
653 $a tracklet linking
653 $a tracklet stitching
653 $a tracking evaluation
653 $a MOT evaluation
653 $a Bayesian recursive filter
653 $a Bayesian multi-target tracking
653 $a Bayesian multi-object tracking
653 $a people tracking
653 $a animal tracking
653 $a group tracking
653 $a tracking bats
653 $a tracking birds
655 4 $a Electronic books $3 382946
700 1 $a Wu, Zheng $c (Computer scientist), $e author $3 437839
776 0 8 $i Print version: $a Betke, Margrit. $t Data association for multi- object visual tracking. $d [San Rafael, California] : Morgan & Claypool, 2017 $z 9781627059558 $w (OCoLC)962485945
830 0 $a Synthesis lectures on computer vision ; $v #9. $x 2153-1056 $3 437840
856 4 0 $u http://portal.igpublish.com/iglibrary/search/MCPB0006279.html

筆 0 讀者評論

多媒體

評論

新增評論分享你的心得，請勿在此評論區張貼涉及人身攻擊、情緒謾罵、或內容涉及非法的不當言論，館方有權利刪除任何違反評論規則之發言，情節嚴重者一律停權，以維護所有讀者的自由言論空間。

Export

取書館別