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Tubelets : Unsupervised action proposals from spatiotemporal super-voxels / Mihir Jain in International journal of computer vision, vol 124 n° 3 (15 September 2017)  

Public [article]  inInternational journal of computer vision > vol 124 n° 3 (15 September 2017) . - pp 287 - 311 Titre : Tubelets : Unsupervised action proposals from spatiotemporal super-voxels Type de document : Article/Communication Auteurs : Mihir Jain, Auteur ; Jan van Gemert, Auteur ; Hervé Jégou, Auteur ; Patrick Bouthemy, Auteur ; Cees G. M. Snoek, Auteur Année de publication : 2017 Article en page(s) : pp 287 - 311 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] données spatiotemporelles [Termes IGN] reconnaissance de gestes [Termes IGN] rectangle englobant minimum [Termes IGN] séquence d'images [Termes IGN] voxel Résumé : (Auteur) This paper considers the problem of localizing actions in videos as sequences of bounding boxes. The objective is to generate action proposals that are likely to include the action of interest, ideally achieving high recall with few proposals. Our contributions are threefold. First, inspired by selective search for object proposals, we introduce an approach to generate action proposals from spatiotemporal super-voxels in an unsupervised manner, we call them Tubelets. Second, along with the static features from individual frames our approach advantageously exploits motion. We introduce independent motion evidence as a feature to characterize how the action deviates from the background and explicitly incorporate such motion information in various stages of the proposal generation. Finally, we introduce spatiotemporal refinement of Tubelets, for more precise localization of actions, and pruning to keep the number of Tubelets limited. We demonstrate the suitability of our approach by extensive experiments for action proposal quality and action localization on three public datasets: UCF Sports, MSR-II and UCF101. For action proposal quality, our unsupervised proposals beat all other existing approaches on the three datasets. For action localization, we show top performance on both the trimmed videos of UCF Sports and UCF101 as well as the untrimmed videos of MSR-II. Numéro de notice : A2017-812 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-017-1023-9 En ligne : https://doi.org/10.1007/s11263-017-1023-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89252 [article]