Distances evolution analysis for online and off-line human object interaction recognition

Meng, Meng; Drira, Hassen; Boonaert, Jacques

Document type :

Compte-rendu et recension critique d'ouvrage

Title :

Distances evolution analysis for online and off-line human object interaction recognition

Author(s) :

Meng, Meng [Auteur]
Tianjin University of Science and Technology [TUST]
Drira, Hassen [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Boonaert, Jacques [Auteur]
Centre for Digital Systems [CERI SN - IMT Nord Europe]

Journal title :

Image and Vision Computing

Publisher :

Elsevier

Publication date :

2017-12

ISSN :

0262-8856

English keyword(s) :

Human object interaction
rate invariance
shape analysis
temporal modeling

HAL domain(s) :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]

English abstract : [en]

Human action recognition in 3D sequences is one of the most challenging and active areas of research in the computer vision domain. However designing automatic systems that are robust to significant variability due to ...
Show more >Human action recognition in 3D sequences is one of the most challenging and active areas of research in the computer vision domain. However designing automatic systems that are robust to significant variability due to object combinations and high complexity of human motions are more challenging in addition to the typical requirements such as rotation, translation, and scale invariance is challenging task. In this paper, we propose a spatio-temporal modeling of human-object interaction videos for on-line and off-line recognition. The inter joint distances and the object are considered as low-level features for online classification. For off-line recognition, we propose rate-invariant classification of full video and early recognition. A shape analysis of trajectories of the inter-joint and object-joints distances is proposed for this end. The experiments conducted following state-of-the-art settings using MSR Daily Activity 3D Dataset and On-line RGBD Action Dataset and on a new Multi-view dataset for human object interaction demonstrate that the proposed approach is effective and discrimina-tive for human object interaction classification as demonstrated here.Show less >

Language :

Anglais

Popular science :

Non

Collections :