Cross-View Action Recognition from Temporal Self-Similarities

Imran Junejo 1 Emilie Dexter 1 Ivan Laptev 1 Patrick Pérez 1
1 VISTA - Vision spatio-temporelle et active
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : This paper concerns recognition of human actions under view changes. We explore self-similarities of action sequences over time and observe the striking stability of such measures across views. Building upon this key observation we develop an action descriptor that captures the structure of temporal similarities and dissimilarities within an action sequence. Despite this descriptor not being strictly view-invariant, we provide intuition and experimental validation demonstrating the high stability of self-similarities under view changes. Self-similarity descriptors are also shown stable under action variations within a class as well as discriminative for action recognition. Interestingly, self-similarities computed from different image features possess similar properties and can be used in a complementary fashion. Our method is simple and requires neither structure recovery nor multi-view correspondence estimation. Instead, it relies on weak geometric cues captured by self-similarities and combines them with machine learning for efficient cross-view action recognition. The method is validated on three public datasets, it has similar or superior performance compared to related methods and it performs well even in extreme conditions such as when recognizing actions from top views while using side views for training only.
Type de document :
[Research Report] PI 1895, 2008, pp.19
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Soumis le : lundi 23 juin 2008 - 13:25:36
Dernière modification le : mercredi 21 février 2018 - 01:48:56
Document(s) archivé(s) le : vendredi 28 mai 2010 - 19:42:39


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  • HAL Id : inria-00289708, version 1



Imran Junejo, Emilie Dexter, Ivan Laptev, Patrick Pérez. Cross-View Action Recognition from Temporal Self-Similarities. [Research Report] PI 1895, 2008, pp.19. 〈inria-00289708〉



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