Modeling Patterns of Activity and Detecting Abnormal Events with Low-level Co-occurrences

Abstract : We explore in this chapter a location-based approach for behavior modeling and abnormality detection. In contrast to conventional object-based approaches for which objects are identified, classified, and tracked to locate objects with suspicious behavior, we proceed directly with event characterization and behavior modeling using low-level features. Our approach consists of two-phases. In the first phase, co-occurence of activity between temporal sequences of motion labels are used to build a statistical model for normal behavior. This model of co-occurrence statistics is embedded within a co-occurence matrix which accounts for spatio-temporal co-occurence of activity. In the second phase, the co-occurence matrix is used as a potential function in a Markov Random Field framework to describe, as the video streams in, the probability of observing new volumes of activity. The co-occurence matrix is thus used for detecting moving objects whose behavior differs from the ones observed during the training phase. Interestingly, the Markov Random Field distribution implicitly accounts for speed, direction, as well as the average size of the objects without any higher-level intervention. Furthermore, when the spatio-temporal volume is large enough, the co-occurrence distribution contains the average normal path followed by moving objects. Our method has been tested on various outdoor videos representing various challenges.
Type de document :
Chapitre d'ouvrage
Bhanu, B. and Ravishankar, C.V. and Roy-Chowdhury, A.K. and Aghajan, H. and Terzopoulos, D. Distributed Video Sensor Networks, Springer, 2011, 978-0-85729-126-4
Liste complète des métadonnées

Littérature citée [21 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00545497
Contributeur : Yannick Benezeth <>
Soumis le : mardi 16 octobre 2012 - 15:16:14
Dernière modification le : mercredi 17 octobre 2012 - 13:33:39
Document(s) archivé(s) le : mardi 13 décembre 2016 - 17:36:21

Fichier

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

Identifiants

  • HAL Id : inria-00545497, version 1

Citation

Yannick Benezeth, Pierre-Marc Jodoin, Venkatesh Saligrama. Modeling Patterns of Activity and Detecting Abnormal Events with Low-level Co-occurrences. Bhanu, B. and Ravishankar, C.V. and Roy-Chowdhury, A.K. and Aghajan, H. and Terzopoulos, D. Distributed Video Sensor Networks, Springer, 2011, 978-0-85729-126-4. 〈inria-00545497〉

Partager

Métriques

Consultations de la notice

74

Téléchargements de fichiers

126