Moving Objects Detection Using Machine Learning (Paperback, 1st ed. 2022)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	SpringerBriefs in Electrical and Computer Engineering
Release date:	December 2021
First published:	2022
Authors:	Navneet Ghedia • Chandresh Vithalani • Ashish M. Kothari • Rohit M. Thanki
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	85
Edition:	1st ed. 2022
ISBN-13:	978-3-03-090909-3
Categories:	Books > Professional & Technical > Electronics & communications engineering > Communications engineering / telecommunications > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision Promotions
LSN:	3-03-090909-3
Barcode:	9783030909093

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