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008			230925s2023 si | o |||| 0|eng d
020			_a9789819932887
024	7		_a10.1007/978-981-99-3288-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1634 _b2023 EB
245	0	0	_aObject Tracking Technology : _bTrends, Challenges and Applications _cedited by Ashish Kumar, Rachna Jain, Ajantha Devi Vairamani, Anand Nayyar
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aContributions to Environmental Sciences & Innovative Business Technology _x2731-8311
505	0		_aSingle Object Detection from Video Streaming -- Different Approaches to Background Subtraction and Object Tracking in Video Streams: A Review -- Auto Alignment of Tanker Loading Arm Utilizing Stereo-Vision Video and 3D Euclidean Scene Reconstruction -- Visual Object Segmentation Improvement using Deep Convolutional Neural Networks -- Applications of Deep Learning based Methods on Surveillance Video Stream by Tracking Various Suspicious Activities -- Hardware Design Aspects of Visual Tracking System -- Automatic Helmet (Object) Detection and Tracking the Riders using Kalman Filter Technique -- Deep Learning based Multi-Object Tracking -- Multiple Object Tracking of Autonomous Vehicles for Sustainable and Smart Cities -- Multi Object Detection: A Social Distancing Monitoring System -- Investigating Two Stage Detection Methods Using Traffic Light Detection Dataset.
520			_aWith the increase in urban population, it became necessary to keep track of the object of interest. In favor of SDGs for sustainable smart city, with the advancement in technology visual tracking extends to track multi-target present in the scene rather estimating location for single target only. In contrast to single object tracking, multi-target introduces one extra step of detection. Tracking multi-target includes detecting and categorizing the target into multiple classes in the first frame and provides each individual target an ID to keep its track in the subsequent frames of a video stream. One category of multi-target algorithms exploits global information to track the target of the detected target. On the other hand, some algorithms consider present and past information of the target to provide efficient tracking solutions. Apart from these, deep leaning-based algorithms provide reliable and accurate solutions. But, these algorithms are computationally slow when applied in real-time. This book presents and summarizes the various visual tracking algorithms and challenges in the domain. The various feature that can be extracted from the target and target saliency prediction is also covered. It explores a comprehensive analysis of the evolution from traditional methods to deep learning methods, from single object tracking to multi-target tracking. In addition, the application of visual tracking and the future of visual tracking can also be introduced to provide the future aspects in the domain to the reader. This book also discusses the advancement in the area with critical performance analysis of each proposed algorithm. This book will be formulated with intent to uncover the challenges and possibilities of efficient and effective tracking of single or multi-object, addressing the various environmental and hardware challenges. The intended audience includes academicians, engineers, postgraduate students, developers, professionals, military personals, scientists, data analysts, practitioners, and people who are interested in exploring more about tracking.· Another projected audience are the researchers and academicians who identify and develop methodologies, frameworks, tools, and applications through reference citations, literature reviews, quantitative/qualitative results, and discussions.
988			_aSpringer_Computer_2023
650		7	_2embne _9159793 _aVisión por ordenador
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-99-3288-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _eb _zSI