| 书目名称 | Moving Objects Detection Using Machine Learning |
| 编辑 | Navneet Ghedia,Chandresh Vithalani,Rohit M. Thanki |
| 视频video | |
| 概述 | Provides basic information on object detection and tracking in a digital video stream using various background separation and learning based approaches.Presents information about various systems, whic |
| 丛书名称 | SpringerBriefs in Electrical and Computer Engineering |
| 图书封面 |  |
| 描述 | 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.. |
| 出版日期 | Book 2022 |
| 关键词 | object detection and tracking; unsupervised learning algorithm; Kalman filter; separation algorithms; Va |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-90910-9 |
| isbn_softcover | 978-3-030-90909-3 |
| isbn_ebook | 978-3-030-90910-9Series ISSN 2191-8112 Series E-ISSN 2191-8120 |
| issn_series | 2191-8112 |
| copyright | The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 |
发表于 2025-3-21 17:45:45
