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Space-time tree ensemble for action recognition and localization / Shugao Ma in International journal of computer vision, vol 126 n° 2-4 (April 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 2-4 (April 2018) . - pp 314 - 332 Titre : Space-time tree ensemble for action recognition and localization Type de document : Article/Communication Auteurs : Shugao Ma, Auteur ; Jianming Zhang, Auteur ; Stan Sclaroff, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 314 - 332 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] arbre (mathématique) [Termes IGN] géopositionnement [Termes IGN] reconnaissance de gestes Résumé : (Auteur) Human actions are, inherently, structured patterns of body movements. We explore ensembles of hierarchical spatio-temporal trees, discovered directly from training data, to model these structures for action recognition and spatial localization. Discovery of frequent and discriminative tree structures is challenging due to the exponential search space, particularly if one allows partial matching. We address this by first building a concise action word vocabulary via discriminative clustering of the hierarchical space-time segments, which is a two-level video representation that captures both static and non-static relevant space-time segments of the video. Using this vocabulary we then utilize tree mining with subsequent tree clustering and ranking to select a compact set of discriminative tree patterns. Our experiments show that these tree patterns, alone, or in combination with shorter patterns (action words and pairwise patterns) achieve promising performance on three challenging datasets: UCF Sports, HighFive and Hollywood3D. Moreover, we perform cross-dataset validation, using trees learned on HighFive to recognize the same actions in Hollywood3D, and using trees learned on UCF-Sports to recognize and localize the similar actions in JHMDB. The results demonstrate the potential for cross-dataset generalization of the trees our approach discovers. Numéro de notice : A2018-407 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-016-0980-8 Date de publication en ligne : 02/02/2017 En ligne : https://doi.org/10.1007/s11263-016-0980-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90880 [article]