{"product_id":"visual-object-tracking-using-deep-learning-9781032490533","title":"Visual Object Tracking using Deep Learning","description":"\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eThis book discusses conventional and advanced methods of visual tracking, including stochastic, deterministic, generative, and discriminative techniques, and explores deep learning-based trackers and correlation filter-based trackers. It also covers potential performance metrics, the salient features of deep learning trackers, and the future research directions for visual tracking. The text is intended for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology. \u003c\/blockquote\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Unspecified\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 202 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 20 November 2023\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Taylor \u0026amp; Francis Ltd\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003eThis comprehensive book delves into both conventional and advanced methods of visual tracking, providing a detailed description of these techniques. In the conventional methods section, the reader will explore a range of visual tracking techniques, including stochastic, deterministic, generative, and discriminative approaches. These methods are further examined in the context of multi-stage and collaborative frameworks, highlighting their applications and limitations.\u003cbr\u003e\u003cbr\u003eThe advanced methods section delves into the realm of deep learning-based trackers and correlation filter-based trackers. It provides a detailed analysis of these categories, exploring their key features, advantages, and limitations. The book also discusses potential performance metrics used to compare the efficiency and effectiveness of different visual tracking methods.\u003cbr\u003e\u003cbr\u003eFurthermore, the book elaborates on the salient features of deep learning trackers, where handcrafted features are fused to reduce computational complexity. It illustrates various categories of correlation filter-based trackers that are suitable for superior and efficient performance under tedious tracking scenarios. The book also explores the future research directions for visual tracking by analyzing real-time applications in various fields.\u003cbr\u003e\u003cbr\u003eThe book is designed to cater to senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology. It offers a comprehensive and in-depth exploration of visual tracking techniques, providing valuable insights and knowledge for those interested in this area.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 557g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 234 x 156 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9781032490533\u003c\/p\u003e","brand":"AshishKumar","offers":[{"title":"Unspecified","offer_id":44842390257914,"sku":"9781032490533","price":99.96,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/1701452702478_book.jpg?v=1701690448","url":"https:\/\/shulphink.com\/products\/visual-object-tracking-using-deep-learning-9781032490533","provider":"Shulph Ink","version":"1.0","type":"link"}