High-dimensional statistical measure for region-of-interest tracking

Abstract : This paper deals with region-of-interest (ROI) tracking in video sequences. The goal is to determine in successive frames the region which best matches, in terms of a similarity measure, a ROI defined in a reference frame. Some tracking methods define similarity measures which efficiently combine several visual features into a probability density function (PDF) representation, thus building a discriminative model of the ROI. This approach implies dealing with PDFs with domains of definition of high dimension. To overcome this obstacle, a standard solution is to assume independence between the different features in order to bring out low-dimension marginal laws and/or to make some parametric assumptions on the PDFs at the cost of generality. We discard these assumptions by proposing to compute the Kullback-Leibler divergence between high-dimensional PDFs using the $k$-th nearest neighbor framework. In consequence, the divergence is expressed directly from the samples, i.e. without explicit estimation of the underlying PDFs. As an application, we defined 5, 7, and 13-dimensional feature vectors containing color information (including pixel-based, gradient-based and patch-based) and spatial layout. The proposed procedure performs tracking allowing for translation and scaling of the ROI. Experiments show its efficiency on a movie excerpt and standard test sequences selected for the specific conditions they exhibit: partial occlusions, variations of luminance, noise, and complex motion.
Type de document :
Article dans une revue
IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2009, 18 (6), pp.1266-1283. <10.1109/TIP.2009.2015158>
Liste complète des métadonnées


https://hal.archives-ouvertes.fr/hal-00417652
Contributeur : Eric Debreuve <>
Soumis le : mercredi 2 avril 2014 - 11:42:20
Dernière modification le : jeudi 3 avril 2014 - 10:33:19
Document(s) archivé(s) le : mercredi 2 juillet 2014 - 10:36:09

Fichier

Boltz2009TIP_PreprintA4.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Sylvain Boltz, Eric Debreuve, Michel Barlaud. High-dimensional statistical measure for region-of-interest tracking. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2009, 18 (6), pp.1266-1283. <10.1109/TIP.2009.2015158>. <hal-00417652>

Partager

Métriques

Consultations de
la notice

145

Téléchargements du document

159