LEADER 00791cam 22002533n 450 001 9910475057303321 005 20210609163717.0 100 $a20210609d1876----kmuy0itay5050 ba 101 0 $afre 102 $aFR 105 $aaf---z--001cy 200 1 $aQuatre ans chez les Achantis$ejournal de MM. Ramseyer et Kuhne [sic] pendant le temps de leur captivité 210 $aParis$cSandoz et Fischbacher$d1876 215 $a514 p., tav.$cill.$d20 cm 610 0 $aAfrica occidentale$aCiviltà indigene 700 1$aRamseyer,$bFritz$0803654 701 1$aKuhne$cmissionnaire$0803655 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910475057303321 952 $a3/XIV AA 1$b140$fFLFBC 959 $aFLFBC 996 $aQuatre ans chez les Achantis$91804967 997 $aUNINA LEADER 03812nam 22005295 450 001 9910350231903321 005 20200703074221.0 010 $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 :$cSpringer Singapore :$cImprint: Springer,$d2019. 215 $a1 online resource (X, 128 p. 115 illus., 44 illus. in color.) 311 $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 $aOptical data processing 606 $aPattern perception 606 $aData mining 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 615 0$aOptical data processing. 615 0$aPattern perception. 615 0$aData mining. 615 14$aImage Processing and Computer Vision. 615 24$aPattern 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