000			03214nam a22004695i 4500
999			_c395884 _d395884
001			395884
003			ES-MaUEC
005			20230128111058.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			230128s2022 sz | s |||| 0|eng d
020			_a9783030909109
024	7		_a10.1007/978-3-030-90910-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2022 EB
100	1		_aGhedia, Navneet _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686274
245	1	0	_aMoving Objects Detection Using Machine Learning _cby Navneet Ghedia, Chandresh Vithalani, Ashish M. Kothari, Rohit M. Thanki
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (VII, 85 páginas) _b29 ilustraciones, 19 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSpringerBriefs in Electrical and Computer Engineering _x2191-8120
505	0		_aChapter1. Introduction -- Chapter2. Existing Research in Video Surveillance System -- Chapter3. Background Modeling -- Chapter4. Object Tracking -- Chapter5. Summary of Book.
520			_aThis 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.
988			_aSpringer_Engineering_2022
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9159793 _aVisión por ordenador
650		7	_2embne _9156182 _aVídeo digital
700	1		_9686275 _aVithalani, Chandresh _eautor
700			_9670707 _aKothari, Ashish _eautor
700			_9670706 _aThanki, Rohit M. _eautor
776	0	8	_iPrinted edition: _z9783030909093
776	0	8	_iPrinted edition: _z9783030909116
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-90910-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eIG _zSI