國立虎尾科技大學 |

Moving Objects Detection Using Machine Learning

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Moving Objects Detection Using Machine Learning/ by Navneet Ghedia, Chandresh Vithalani, Ashish M. Kothari, Rohit M. Thanki.
作者:	Ghedia, Navneet.
其他作者:	Thanki, Rohit M.
面頁冊數:	VII, 85 p. 29 illus., 19 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational Intelligence. -
電子資源:	https://doi.org/10.1007/978-3-030-90910-9
ISBN:	9783030909109

Moving Objects Detection Using Machine Learning
Ghedia, Navneet.

Moving Objects Detection Using Machine Learning[electronic resource] /by Navneet Ghedia, Chandresh Vithalani, Ashish M. Kothari, Rohit M. Thanki. - 1st ed. 2022. - VII, 85 p. 29 illus., 19 illus. in color.online resource. - SpringerBriefs in Electrical and Computer Engineering,2191-8120. - SpringerBriefs in Electrical and Computer Engineering,.

Chapter1. Introduction -- Chapter2. Existing Research in Video Surveillance System -- Chapter3. Background Modeling -- Chapter4. Object Tracking -- Chapter5. Summary of Book.

This book shows how machine learning can detect moving objects in a digital video stream. The authors present different background subtraction approaches, foreground segmentation, and object tracking approaches to accomplish this. They also propose an algorithm that considers a multimodal background subtraction approach that can handle a dynamic background and different constraints. The authors show how the proposed algorithm is able to detect and track 2D & 3D objects in monocular sequences for both indoor and outdoor surveillance environments and at the same time, also able to work satisfactorily in a dynamic background and with challenging constraints. In addition, the shows how the proposed algorithm makes use of parameter optimization and adaptive threshold techniques as intrinsic improvements of the Gaussian Mixture Model. The presented system in the book is also able to handle partial occlusion during object detection and tracking. All the presented work and evaluations were carried out in offline processing with the computation done by a single laptop computer with MATLAB serving as software environment.

ISBN: 9783030909109

Standard No.: 10.1007/978-3-030-90910-9doiSubjects--Topical Terms:

768837
Computational Intelligence.

LC Class. No.: TK5101-5105.9

Dewey Class. No.: 621.382

Moving Objects Detection Using Machine Learning
LDR:02724nam a22003975i 4500 001 1091992
003 DE-He213
005 20220114074301.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783030909109 $9 978-3-030-90910-9
024 7 $a 10.1007/978-3-030-90910-9 $2 doi
035 $a 978-3-030-90910-9
050 4 $a TK5101-5105.9
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 621.382 $2 23
100 1 $a Ghedia, Navneet. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1399692
245 1 0 $a Moving Objects Detection Using Machine Learning $h [electronic resource] / $c by Navneet Ghedia, Chandresh Vithalani, Ashish M. Kothari, Rohit M. Thanki.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a VII, 85 p. 29 illus., 19 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in Electrical and Computer Engineering, $x 2191-8120
505 0 $a Chapter1. Introduction -- Chapter2. Existing Research in Video Surveillance System -- Chapter3. Background Modeling -- Chapter4. Object Tracking -- Chapter5. Summary of Book.
520 $a This book shows how machine learning can detect moving objects in a digital video stream. The authors present different background subtraction approaches, foreground segmentation, and object tracking approaches to accomplish this. They also propose an algorithm that considers a multimodal background subtraction approach that can handle a dynamic background and different constraints. The authors show how the proposed algorithm is able to detect and track 2D & 3D objects in monocular sequences for both indoor and outdoor surveillance environments and at the same time, also able to work satisfactorily in a dynamic background and with challenging constraints. In addition, the shows how the proposed algorithm makes use of parameter optimization and adaptive threshold techniques as intrinsic improvements of the Gaussian Mixture Model. The presented system in the book is also able to handle partial occlusion during object detection and tracking. All the presented work and evaluations were carried out in offline processing with the computation done by a single laptop computer with MATLAB serving as software environment.
650 2 4 $a Computational Intelligence. $3 768837
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 671334
650 1 4 $a Communications Engineering, Networks. $3 669809
650 0 $a Computational intelligence. $3 568984
650 0 $a Computer vision. $3 561800
650 0 $a Image processing—Digital techniques. $3 1365735
650 0 $a Telecommunication. $3 568341
700 1 $a Thanki, Rohit M. $e author. $0 (orcid)0000-0002-0645-6266 $1 https://orcid.org/0000-0002-0645-6266 $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1288748
700 1 $a Kothari, Ashish M. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1300459
700 1 $a Vithalani, Chandresh. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1399693
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030909093
776 0 8 $i Printed edition: $z 9783030909116
830 0 $a SpringerBriefs in Electrical and Computer Engineering, $x 2191-8112 $3 1253713
856 4 0 $u https://doi.org/10.1007/978-3-030-90910-9
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)