Uncertainty control for reliable video understanding on complex environments

Abstract : The most popular applications for video understanding are those related to video-surveillance (e.g. alarms, abnormal behaviours, expected events, access control). Video understanding has several other applications of high impact to the society as medical supervision, traffic control, violent acts detection, crowd behaviour analysis, among many others. We propose a new generic video understanding approach able to extract and learn valuable information from noisy video scenes for real-time applications. This approach comprises motion segmentation, object classification, tracking and event learning phases. This work is focused on building the first fundamental blocks allowing a proper management of uncertainty of data in every phase of the video understanding process. The main contributions of the proposed approach are: (i) a new algorithm for tracking multiple objects in noisy environments, (ii) the utilisation of reliability measures for modelling uncertainty in data and for proper selection of valuable information extracted from noisy data, (iii) the improved capability of tracking to manage multiple visual evidence-target associations, (iv) the combination of 2D image data with 3D information in a dynamics model governed by reliability measures for proper control of uncertainty in data, and (v) a new approach for event recognition through incremental event learning, driven by reliability measures for selecting the most stable and relevant data.
Type de document :
Chapitre d'ouvrage
Weiyao Lin. Video Surveillance, InTech, 2011, 978-953-307-436-8. 〈10.5772/15606〉
Liste complète des métadonnées

Littérature citée [32 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-00696408
Contributeur : Francois Bremond <>
Soumis le : dimanche 13 mai 2012 - 22:30:09
Dernière modification le : jeudi 11 janvier 2018 - 16:20:46
Document(s) archivé(s) le : vendredi 30 novembre 2012 - 11:36:30

Fichier

ZunInTech2010.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Marcos Zuniga, Francois Bremond, Monique Thonnat. Uncertainty control for reliable video understanding on complex environments. Weiyao Lin. Video Surveillance, InTech, 2011, 978-953-307-436-8. 〈10.5772/15606〉. 〈hal-00696408〉

Partager

Métriques

Consultations de la notice

412

Téléchargements de fichiers

138