Body parts detection for people tracking using trees of Histogram of Oriented Gradient descriptors

Etienne Corvee 1 François Bremond 1
1 PULSAR - Perception Understanding Learning Systems for Activity Recognition
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Vision algorithms face many challenging issues when it comes to analyze human activities in video surveillance applications. For instance, occlusions makes the detection and tracking of people a hard task to perform. Hence advanced and adapted solutions are required to analyze the content of video sequences. We here present a people detection algorithm based on a hierarchical tree of Histogram of Oriented Gradients referred to as HOG. The detection is coupled with independently trained body part detectors to enhance the detection performance and to reach state of the art performances. We adopt a person tracking scheme which calculates HOG dissimilarities between detected persons throughout a sequence. The algorithms are tested in videos with challenging situations such as occlusions. False alarms are further reduced by using 2D and 3D information of moving objects segmented from a background reference frame.
Type de document :
Communication dans un congrès
AVSS - 7th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Sep 2010, Boston, United States. 2010
Liste complète des métadonnées

Littérature citée [21 références]  Voir  Masquer  Télécharger

detect-track.jpeg
detect-track.jpeg
https://hal.inria.fr/inria-00496980
Contributeur : Etienne Corvee <>
Soumis le : vendredi 2 juillet 2010 - 10:20:09
Dernière modification le : samedi 27 janvier 2018 - 01:30:48
Document(s) archivé(s) le : lundi 4 octobre 2010 - 12:06:15

Fichiers

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

Identifiants

  • HAL Id : inria-00496980, version 1

Collections

INRIA

Citation

Etienne Corvee, François Bremond. Body parts detection for people tracking using trees of Histogram of Oriented Gradient descriptors. AVSS - 7th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Sep 2010, Boston, United States. 2010. 〈inria-00496980〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

506

Téléchargements de fichiers

1197

Contact


archive-ouverte@inria.fr