Learning Action Primitives for Multi-level Video Event Understanding

Autor:	Zhiwei Deng, Greg Mori, Lei Chen, Tian Lan, Guang-Tong Zhou
Rok vydání:	2015
Předmět:	Action (philosophy) Discriminative model business.industry Computer science Event (computing) Variation (game tree) Artificial intelligence Mixture model Set (psychology) Machine learning computer.software_genre business computer
Zdroj:	Computer Vision-ECCV 2014 Workshops ISBN: 9783319161983 ECCV Workshops (3)
Popis:	Human action categories exhibit significant intra-class variation. Changes in viewpoint, human appearance, and the temporal evolution of an action confound recognition algorithms. In order to address this, we present an approach to discover action primitives, sub-categories of action classes, that allow us to model this intra-class variation. We learn action primitives and their interrelations in a multi-level spatio-temporal model for action recognition. Action primitives are discovered via a data-driven clustering approach that focuses on repeatable, discriminative sub-categories. Higher-level interactions between action primitives and the actions of a set of people present in a scene are learned. Empirical results demonstrate that these action primitives can be effectively localized, and using them to model action classes improves action recognition performance on challenging datasets.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4b9c1d761cd49fdd946b5e241dd370af https://doi.org/10.1007/978-3-319-16199-0_7 Zobrazit plný text záznamu