Tld and struck: A feature descriptors comparative study (Contributo in volume (capitolo o saggio))

Type
Label
  • Tld and struck: A feature descriptors comparative study (Contributo in volume (capitolo o saggio)) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-319-13323-2_5 (literal)
Alternative label
  • Adamo F.; Carcagni P.; Mazzeo P.L.; Distante C.; Spagnolo P. (2014)
    Tld and struck: A feature descriptors comparative study
    in Activity Monitoring by Multiple Distributed Sensing : Second International Workshop, AMMDS 2014, Stockholm, Sweden, August 24, 2014, Revised Selected Papers, 2014
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Adamo F.; Carcagni P.; Mazzeo P.L.; Distante C.; Spagnolo P. (literal)
Pagina inizio
  • 52 (literal)
Pagina fine
  • 63 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.scopus.com/inward/record.url?eid=2-s2.0-84912134864&partnerID=q2rCbXpz (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Activity Monitoring by Multiple Distributed Sensing : Second International Workshop, AMMDS 2014, Stockholm, Sweden, August 24, 2014, Revised Selected Papers (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 8703 (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • University of Salento, Lecce, Italy; National Research Council of Italy - Institute of Optics, Lecce, Italy (literal)
Titolo
  • Tld and struck: A feature descriptors comparative study (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-319-13322-5 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Pier Luigi Mazzeo; Paolo Spagnolo; Thomas B. Moeslund (literal)
Abstract
  • Object tracking across multiple cameras is a very challenge issue in vision based monitoring applications. The selection of features is the first step to realize a reliable tracking algorithm.,In this work we analyse TLD and Struck, which are two of the most cited real-time visual trackers proposed in the literature in last years. They use two different feature extraction methodologies, Fern and Haar, respectively. The idea of this work is to compare performance of these well known visual tracking algorithms replacing their original feature characterization methods with local feature-based visual representations.,We test the improvement in terms of object detection and tracking performance grafting different features characterization into two completely different online tracker frameworks.,The used feature extraction methods are based on Local Binary Pattern (LBP), Local Gradient Pattern (LGP) and Histogram of Oriented Gradients (HOG). LGP is a novel detection methodology which is insensitive to global intensity variations like other representations such as local binary patterns (LBP).,The experimental results on well known benchmark sequences show as the feature extraction replacing improve the overall performances of the considered real-time visual trackers. (literal)
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