Moving objects detection using de ghedia navneet (20 resultados)

Paperback. Condición: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New.

Condición: New. In English.

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: new. Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Condición: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.

Condición: New.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Taschenbuch. Condición: Neu. Moving Objects Detection Using Machine Learning | Navneet Ghedia (u. a.) | Taschenbuch | SpringerBriefs in Electrical and Computer Engineering | vii | Englisch | 2021 | Springer | EAN 9783030909093 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Condición: new. Questo è un articolo print on demand.

Paperback. Condición: Brand New. 92 pages. 9.25x6.10x9.21 inches. In Stock. This item is printed on demand.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 96 pp. Englisch.…

Condición: New. Print on Demand.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 chal. …

Condición: New. PRINT ON DEMAND.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 96 pp. Englisch.…