國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Analytic combinatorics for multiple ...

Angle, Robert Blair.

Analytic combinatorics for multiple object tracking

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Analytic combinatorics for multiple object trackingby Roy Streit, Robert Blair Angle, Murat Efe.
作者:	Streit, Roy.
其他作者:	Angle, Robert Blair.
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	xvi, 221 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Combinatorial analysis.
電子資源:	https://doi.org/10.1007/978-3-030-61191-0
ISBN:	9783030611910$q(electronic bk.)

Analytic combinatorics for multiple object tracking
Streit, Roy.

Analytic combinatorics for multiple object tracking[electronic resource] /by Roy Streit, Robert Blair Angle, Murat Efe. - Cham :Springer International Publishing :2021. - xvi, 221 p. :ill., digital ;24 cm.

Introduction -- Extended object tracking -- Multiple sensors -- Other high computational complexity tracking problems -- Multiframe assignment and combinatorial optimization -- Saddle Point Method -- Multicomplex Algebra -- Automatic Differentiation -- Conclusion.

The book shows that the analytic combinatorics (AC) method encodes the combinatorial problems of multiple object tracking-without information loss-into the derivatives of a generating function (GF) The book lays out an easy-to-follow path from theory to practice and includes salient AC application examples. Since GFs are not widely utilized amongst the tracking community, the book takes the reader from the basics of the subject to applications of theory starting from the simplest problem of single object tracking, and advancing chapter by chapter to more challenging multi-object tracking problems. Many established tracking filters (e.g., Bayes-Markov, PDA, JPDA, IPDA, JIPDA, CPHD, PHD, multi-Bernoulli, MBM, LMBM, and MHT) are derived in this manner with simplicity, economy, and considerable clarity. The AC method gives significant and fresh insights into the modeling assumptions of these filters and, thereby, also shows the potential utility of various approximation methods that are well established techniques in applied mathematics and physics, but are new to tracking. These unexplored possibilities are reviewed in the final chapter of the book.

ISBN: 9783030611910$q(electronic bk.)

Standard No.: 10.1007/978-3-030-61191-0doiSubjects--Topical Terms:

182280
Combinatorial analysis.

LC Class. No.: QA164 / .S77 2021

Dewey Class. No.: 511.6

Analytic combinatorics for multiple object tracking
LDR:02449nmm a2200337 a 4500 001 595945
003 DE-He213
005 20201126182535.0
006 m d
007 cr nn 008maaau
008 211013s2021 sz s 0 eng d
020 $a 9783030611910$q(electronic bk.)
020 $a 9783030611903$q(paper)
024 7 $a 10.1007/978-3-030-61191-0 $2 doi
035 $a 978-3-030-61191-0
040 $a GP $c GP $e rda
041 0 $a eng
050 4 $a QA164 $b .S77 2021
072 7 $a TTBM $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a TTBM $2 thema
072 7 $a UYS $2 thema
082 0 4 $a 511.6 $2 23
090 $a QA164 $b .S915 2021
100 1 $a Streit, Roy. $3 888503
245 1 0 $a Analytic combinatorics for multiple object tracking $h [electronic resource] / $c by Roy Streit, Robert Blair Angle, Murat Efe.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xvi, 221 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Extended object tracking -- Multiple sensors -- Other high computational complexity tracking problems -- Multiframe assignment and combinatorial optimization -- Saddle Point Method -- Multicomplex Algebra -- Automatic Differentiation -- Conclusion.
520 $a The book shows that the analytic combinatorics (AC) method encodes the combinatorial problems of multiple object tracking-without information loss-into the derivatives of a generating function (GF) The book lays out an easy-to-follow path from theory to practice and includes salient AC application examples. Since GFs are not widely utilized amongst the tracking community, the book takes the reader from the basics of the subject to applications of theory starting from the simplest problem of single object tracking, and advancing chapter by chapter to more challenging multi-object tracking problems. Many established tracking filters (e.g., Bayes-Markov, PDA, JPDA, IPDA, JIPDA, CPHD, PHD, multi-Bernoulli, MBM, LMBM, and MHT) are derived in this manner with simplicity, economy, and considerable clarity. The AC method gives significant and fresh insights into the modeling assumptions of these filters and, thereby, also shows the potential utility of various approximation methods that are well established techniques in applied mathematics and physics, but are new to tracking. These unexplored possibilities are reviewed in the final chapter of the book.
650 0 $a Combinatorial analysis. $3 182280
650 1 4 $a Signal, Image and Speech Processing. $3 273768
650 2 4 $a Probability and Statistics in Computer Science. $3 274053
650 2 4 $a Probability Theory and Stochastic Processes. $3 274061
700 1 $a Angle, Robert Blair. $3 888504
700 1 $a Efe, Murat. $3 888505
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-61191-0
950 $a Engineering (SpringerNature-11647)