Visual Object Tracking Deep (67 results)

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Paperback. Condition: New. This book covers the description of both conventional methods and advanced methods. In conventional methods, visual tracking techniques such as stochastic, deterministic, generative, and discriminative are discussed. The conventional techniques are further explored for multi-stage and collaborative frameworks. In advanced methods, various categories of deep learning-based trackers and correlation filter-based trackers are analyzed. The book also:Discusses potential performance metrics used for comparing the efficiency and effectiveness of various visual tracking methodsElaborates on the salient features of deep learning trackers along with traditional trackers, wherein the handcrafted features are fused to reduce computational complexityIllustrates various categories of correlation filter-based trackers suitable for superior and efficient performance under tedious tracking scenariosExplores the future research directions for visual tracking by analyzing the real-time applicationsThe book comprehensively discusses various deep learning-based tracking architectures along with conventional tracking methods. It covers in-depth analysis of various feature extraction techniques, evaluation metrics and benchmark available for performance evaluation of tracking frameworks. The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.…

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Condition: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

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Taschenbuch. Condition: Neu. Visual Object Tracking using Deep Learning | Ashish Kumar | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | CRC Press | EAN 9781032598079 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu. …

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Paperback. Condition: New. This book covers the description of both conventional methods and advanced methods. In conventional methods, visual tracking techniques such as stochastic, deterministic, generative, and discriminative are discussed. The conventional techniques are further explored for multi-stage and collaborative frameworks. In advanced methods, various categories of deep learning-based trackers and correlation filter-based trackers are analyzed. The book also:Discusses potential performance metrics used for comparing the efficiency and effectiveness of various visual tracking methodsElaborates on the salient features of deep learning trackers along with traditional trackers, wherein the handcrafted features are fused to reduce computational complexityIllustrates various categories of correlation filter-based trackers suitable for superior and efficient performance under tedious tracking scenariosExplores the future research directions for visual tracking by analyzing the real-time applicationsThe book comprehensively discusses various deep learning-based tracking architectures along with conventional tracking methods. It covers in-depth analysis of various feature extraction techniques, evaluation metrics and benchmark available for performance evaluation of tracking frameworks. The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.…

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Taschenbuch. Condition: Neu. Visual Object Tracking from Correlation Filter to Deep Learning | Weiwei Xing (u. a.) | Taschenbuch | xiv | Englisch | 2022 | Springer | EAN 9789811662447 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …