語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Moving objects detection using machi...
~
Ghedia, Navneet.
FindBook
Google Book
Amazon
博客來
Moving objects detection using machine learning
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Moving objects detection using machine learning/ by Navneet Ghedia ... [et al.].
其他作者:
Ghedia, Navneet.
出版者:
Cham :Springer International Publishing : : 2022.,
面頁冊數:
vii, 85 p. :ill. (some col.), digital ;24 cm.
內容註:
Chapter1. Introduction -- Chapter2. Existing Research in Video Surveillance System -- Chapter3. Background Modeling -- Chapter4. Object Tracking -- Chapter5. Summary of Book.
Contained By:
Springer Nature eBook
標題:
Machine learning. -
電子資源:
https://doi.org/10.1007/978-3-030-90910-9
ISBN:
9783030909109
Moving objects detection using machine learning
Moving objects detection using machine learning
[electronic resource] /by Navneet Ghedia ... [et al.]. - Cham :Springer International Publishing :2022. - vii, 85 p. :ill. (some col.), digital ;24 cm. - 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:
533906
Machine learning.
LC Class. No.: Q325.5
Dewey Class. No.: 006.31
Moving objects detection using machine learning
LDR
:02357nmm a2200337 a 4500
001
2297253
003
DE-He213
005
20211216154813.0
006
m d
007
cr nn 008maaau
008
230324s2022 sz s 0 eng d
020
$a
9783030909109
$q
(electronic bk.)
020
$a
9783030909093
$q
(paper)
024
7
$a
10.1007/978-3-030-90910-9
$2
doi
035
$a
978-3-030-90910-9
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
Q325.5
072
7
$a
TJK
$2
bicssc
072
7
$a
TEC041000
$2
bisacsh
072
7
$a
TJK
$2
thema
082
0 4
$a
006.31
$2
23
090
$a
Q325.5
$b
.M935 2022
245
0 0
$a
Moving objects detection using machine learning
$h
[electronic resource] /
$c
by Navneet Ghedia ... [et al.].
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2022.
300
$a
vii, 85 p. :
$b
ill. (some col.), digital ;
$c
24 cm.
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
0
$a
Machine learning.
$3
533906
650
0
$a
Computer vision.
$3
540671
650
0
$a
Video surveillance.
$3
1375317
650
0
$a
Digital video.
$3
588086
650
1 4
$a
Communications Engineering, Networks.
$3
891094
650
2 4
$a
Computer Imaging, Vision, Pattern Recognition and Graphics.
$3
890871
650
2 4
$a
Computational Intelligence.
$3
1001631
700
1
$a
Ghedia, Navneet.
$3
3592633
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer Nature eBook
830
0
$a
SpringerBriefs in electrical and computer engineering.
$3
1565565
856
4 0
$u
https://doi.org/10.1007/978-3-030-90910-9
950
$a
Engineering (SpringerNature-11647)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9439145
電子資源
11.線上閱覽_V
電子書
EB Q325.5
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入