LEADER 00848nam a2200229 i 4500 001 991001752939707536 005 20020507151323.0 008 990922s1970 it ||| | ita 035 $ab11558982-39ule_inst 035 $aLE02724442$9ExL 040 $aDip.to Studi Giuridici$bita 100 1 $aCapocci, Valentino$0209602 245 10$aChristiana II /$cValentino Capocci 260 $aRomae :$bPontificia universitas lateranense,$c1970 300 $ap. [1]-123 ;$c25 cm. 500 $aEstr. da: Studia et documenta historiae et iuris, v. 36, 1970 907 $a.b11558982$b21-09-06$c02-07-02 912 $a991001752939707536 945 $aLE027 ARCHI M 249$g1$iLE027-4206$lle027$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i11760497$z02-07-02 996 $aChristiana II$9895352 997 $aUNISALENTO 998 $ale027$b01-01-99$cm$da $e-$fita$git $h0$i1 LEADER 03571nam 22005295 450 001 9910350231903321 005 20251113190343.0 010 $a981-13-0469-6 024 7 $a10.1007/978-981-13-0469-9 035 $a(CKB)4100000008525628 035 $a(DE-He213)978-981-13-0469-9 035 $a(MiAaPQ)EBC5781515 035 $a(PPN)236520423 035 $a(EXLCZ)994100000008525628 100 $a20190530d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOnline Visual Tracking /$fby Huchuan Lu, Dong Wang 205 $a1st ed. 2019. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2019. 215 $a1 online resource (X, 128 p. 115 illus., 44 illus. in color.) 311 08$a981-13-0468-8 327 $a1. Introduction to visual tracking -- 2. Visual Tracking based on Sparse Representation -- 3. Visual Tracking based on Local Model -- 4. Visual Tracking based on Model Fusion -- 5. Tracking by Segmentation -- 6. Correlation Tracking -- 7. Visual Tracking based on Deep Learning -- 8. Conclusions and Future Work. 330 $aThis book presents the state of the art in online visual tracking, including the motivations, practical algorithms, and experimental evaluations. Visual tracking remains a highly active area of research in Computer Vision and the performance under complex scenarios has substantially improved, driven by the high demand in connection with real-world applications and the recent advances in machine learning. A large variety of new algorithms have been proposed in the literature over the last two decades, with mixed success. Chapters 1 to 6 introduce readers to tracking methods based on online learning algorithms, including sparse representation, dictionary learning, hashing codes, local model, and model fusion. In Chapter 7, visual tracking is formulated as a foreground/background segmentation problem, and tracking methods based on superpixels and end-to-end deep networks are presented. In turn, Chapters 8 and 9 introduce the cutting-edge tracking methods based on correlation filter and deep learning. Chapter 10 summarizes the book and points out potential future research directions for visual tracking. The book is self-contained and suited for all researchers, professionals and postgraduate students working in the fields of computer vision, pattern recognition, and machine learning. It will help these readers grasp the insights provided by cutting-edge research, and benefit from the practical techniques available for designing effective visual tracking algorithms. Further, the source codes or results of most algorithms in the book are provided at an accompanying website. 606 $aComputer vision 606 $aPattern recognition systems 606 $aData mining 606 $aComputer Vision 606 $aAutomated Pattern Recognition 606 $aData Mining and Knowledge Discovery 615 0$aComputer vision. 615 0$aPattern recognition systems. 615 0$aData mining. 615 14$aComputer Vision. 615 24$aAutomated Pattern Recognition. 615 24$aData Mining and Knowledge Discovery. 676 $a006.6 676 $a006.37 700 $aLu$b Huchuan$4aut$4http://id.loc.gov/vocabulary/relators/aut$01065079 702 $aWang$b Dong$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910350231903321 996 $aOnline Visual Tracking$92543128 997 $aUNINA