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Titlebook: Object Tracking Technology; Trends, Challenges a Ashish Kumar,Rachna Jain,Anand Nayyar Book 2023 The Editor(s) (if applicable) and The Auth

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發(fā)表于 2025-3-21 19:12:30 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Object Tracking Technology
副標(biāo)題Trends, Challenges a
編輯Ashish Kumar,Rachna Jain,Anand Nayyar
視頻videohttp://file.papertrans.cn/701/700157/700157.mp4
概述Elaborates the existing challenges in both single object tracking as well as multi-object tracking.Presents‘the latest comparison of the traditional work with the deep learning based solutions.Compare
叢書(shū)名稱Contributions to Environmental Sciences & Innovative Business Technology
圖書(shū)封面Titlebook: Object Tracking Technology; Trends, Challenges a Ashish Kumar,Rachna Jain,Anand Nayyar Book 2023 The Editor(s) (if applicable) and The Auth
描述With 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 i
出版日期Book 2023
關(guān)鍵詞Target saliency; Single object tracking; Handcrafted features; Deep learning; Multi-object tracking; Dete
版次1
doihttps://doi.org/10.1007/978-981-99-3288-7
isbn_softcover978-981-99-3290-0
isbn_ebook978-981-99-3288-7Series ISSN 2731-8303 Series E-ISSN 2731-8311
issn_series 2731-8303
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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