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Unsupervised detection of ruptures in spatial relationships in video sequences based on log‑likelihood ratio / Abdalbassir Abou-Elailah in Pattern Analysis and Applications, vol 21 n° 3 (August 2018)  

Public [article]  inPattern Analysis and Applications > vol 21 n° 3 (August 2018) . - pp 829 - 846 Titre : Unsupervised detection of ruptures in spatial relationships in video sequences based on log‑likelihood ratio Type de document : Article/Communication Auteurs : Abdalbassir Abou-Elailah , Auteur ; Isabelle Bloch, Auteur ; Valérie Gouet-Brunet , Auteur Année de publication : 2018 Projets : 1-Pas de projet / Article en page(s) : pp 829 - 846 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] détection d'événement [Termes IGN] distance (mathématique) [Termes IGN] histogramme [Termes IGN] modèle linéaire [Termes IGN] primitive [Termes IGN] relation spatiale [Termes IGN] séquence d'images Résumé : (Auteur) In this work, we propose a new approach to automatically detect ruptures in spatial relationships in video sequences, based on low-level primitives, in unsupervised manner. The spatial relationships between two objects of interest are modeled using angle and distance histograms as examples. The evolution of the spatial relationships during time is estimated from the distances between two successive angle or distance histograms and then considered as a temporal signal. The evolution of a spatial relationship is modeled by a linear Gaussian model. Then, two hypotheses “without change” and “with change” are considered, and a log-likelihood ratio is computed. The distribution of the log-likelihood ratio, given that H0 is true, is estimated and used to compute the p value. The comparison of this p value to a significance level 훼, expressing the probability of false alarms, allows us to detect significant ruptures in spatial relationships during time. In addition, this approach is generalized to detect multiple object events such as merging, splitting, and other events that contain ruptures in their spatial relationships evolution. This work shows that the description of spatial relationships across time is a promising step toward event detection. Numéro de notice : A2017-776 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10044-017-0669-9 Date de publication en ligne : 18/11/2017 En ligne : https://doi.org/10.1007/s10044-017-0669-9 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88974 [article]

Detection of abrupt changes in spatial relationships in video sequences / Abdalbassir Abou-Elailah (2015)    

Public Titre : Detection of abrupt changes in spatial relationships in video sequences Type de document : Article/Communication Auteurs : Abdalbassir Abou-Elailah , Auteur ; Valérie Gouet-Brunet , Auteur ; Isabelle Bloch, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2015 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 9493 Projets : DESCRiBE / Dubuisson, Séverine Conférence : ICPRAM 2015, 4th International Conference on Pattern Recognition Applications and Methods 10/01/2015 12/01/2015 Lisbonne Portugal Proceedings Springer Importance : pp 89 - 106 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] détection de changement [Termes IGN] distance euclidienne [Termes IGN] histogramme [Termes IGN] image vidéo [Termes IGN] objet flou [Termes IGN] relation spatiale [Termes IGN] séquence d'images Résumé : (auteur) The purpose of this work is to detect strong changes in spatial relationships between objects in video sequences, with a limited knowledge on the objects. First, a fuzzy representation of the objects is proposed based on low-level generic primitives. Furthermore, angle and distance histograms are used as examples to model the spatial relationships between two objects. Then, we estimate the distances between different angle or distance histograms during time. By analyzing the evolution of the spatial relationships during time, ruptures are detected in this evolution. Experimental results show that the proposed method can efficiently detect the ruptures in the spatial relationships, exploiting only low-level primitives. This constitutes a promising step towards event detection in videos, with few a priori models on the objects. Numéro de notice : C2015-015 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-319-27677-9_6 Date de publication en ligne : 09/01/2016 En ligne : https://doi.org/10.1007/978-3-319-27677-9_6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83206

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