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020 _a9789819932887
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024 7 _a10.1007/978-981-99-3288-7
_2doi
050 4 _aTA1637-1638
072 7 _aTJF
_2bicssc
072 7 _aUYT
_2bicssc
072 7 _aCOM012050
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072 7 _aTJF
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082 0 4 _a621.382
_223
245 1 0 _aObject Tracking Technology
_h[electronic resource] :
_bTrends, Challenges and Applications /
_cedited by Ashish Kumar, Rachna Jain, Ajantha Devi Vairamani, Anand Nayyar.
250 _a1st ed. 2023.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2023.
300 _aXIV, 274 p. 104 illus., 83 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.ยท 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.
650 0 _aImage processing.
650 0 _aImage processing
_xDigital techniques.
650 0 _aComputer vision.
650 0 _aMachine learning.
650 1 4 _aImage Processing.
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 2 4 _aMachine Learning.
700 1 _aKumar, Ashish.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aJain, Rachna.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aVairamani, Ajantha Devi.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aNayyar, Anand.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819932870
776 0 8 _iPrinted edition:
_z9789819932894
776 0 8 _iPrinted edition:
_z9789819932900
830 0 _aContributions to Environmental Sciences & Innovative Business Technology,
_x2731-8311
856 4 0 _uhttps://doi.org/10.1007/978-981-99-3288-7
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c185138
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