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Moving objects detection using machi...

Ghedia, Navneet.

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Moving objects detection using machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Moving objects detection using machine learning/ by Navneet Ghedia ... [et al.].
other author:	Ghedia, Navneet.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	vii, 85 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Chapter1. Introduction -- Chapter2. Existing Research in Video Surveillance System -- Chapter3. Background Modeling -- Chapter4. Object Tracking -- Chapter5. Summary of Book.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	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
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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)