Learning to parse pictures of people - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2002

Learning to parse pictures of people

Résumé

Detecting people in images is a key problem for video indexing, browsing and retrieval. The main difficulties are the large appearance variations caused by action, clothing, illumination, viewpoint and scale. Our goal is to find people in static video frames using learned models of both the appearance of body parts (head, limbs, hands), and of the geometry of their assemblies. We build on Forsyth & Fleck's general ‘body plan' methodology and Felzenszwalb & Huttenlocher's dynamic programming approach for efficiently assembling candidate parts into ‘pictorial structures'. However we replace the rather simple part detectors used in these works with dedicated detectors learned for each body part using Support Vector Machines (SVMs) or Relevance Vector Machines (RVMs). We are not aware of any previous work using SVMs to learn articulated body plans, however they have been used to detect both whole pedestrians and combinations of rigidly positioned subimages (typically, upper body, arms, and legs) in street scenes, under a wide range of illumination, pose and clothing variations. RVMs are SVM-like classifiers that offer a well-founded probabilistic interpretation and improved sparsity for reduced computation.We demonstrate their benefits experimentally in a series of results showing great promise for learning detectors in more general situations.
Fichier principal
Vignette du fichier
RST02.pdf (384.73 Ko) Télécharger le fichier
Vignette du fichier
rst2002.png (319.59 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Format : Figure, Image
Loading...

Dates et versions

inria-00545109 , version 1 (09-12-2010)

Identifiants

  • HAL Id : inria-00545109 , version 1

Citer

Rémi Ronfard, Cordelia Schmid, William Triggs. Learning to parse pictures of people. European Conference on Computer Vision, 2002, Copenhague, Denmark. pp.700--714. ⟨inria-00545109⟩
759 Consultations
384 Téléchargements

Partager

Gmail Facebook X LinkedIn More