000			03650nam a22006015i 4500
999			_c200457984 _d76196
003			TR-AnTOB
005			20231121163223.0
007			cr nn 008mamaa
008			211216s2022 sz | s |||| 0|eng d
020			_a9783030909109
024	7		_a10.1007/978-3-030-90910-9 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041			_aeng
050		4	_aQ325.5
072		7	_aTJK _2bicssc
072		7	_aTEC041000 _2bisacsh
072		7	_aTJK _2thema
090			_aQ325.5EBK
100	1		_aGhedia, Navneet. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aMoving Objects Detection Using Machine Learning _h[electronic resource] / _cby Navneet Ghedia, Chandresh Vithalani, Ashish M. Kothari, Rohit M. Thanki.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_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.
650		0	_aTelecommunication.
650		0	_aImage processing—Digital techniques.
650		0	_aComputer vision.
650		0	_aComputational intelligence.
650	1	4	_aCommunications Engineering, Networks.
650	2	4	_aComputer Imaging, Vision, Pattern Recognition and Graphics.
650	2	4	_aComputational Intelligence.
653		0	_aComputer vision
653		0	_aDigital video
653		0	_aMachine learning
653		0	_aVideo surveillance
700	1		_aVithalani, Chandresh. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aKothari, Ashish M. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aThanki, Rohit M. _eauthor. _0(orcid)0000-0002-0645-6266 _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
830		0	_aSpringerBriefs in Electrical and Computer Engineering, _x2191-8120
856	4	0	_uhttps://doi.org/10.1007/978-3-030-90910-9 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK