Cross-View Action Recognition from Temporal Self-similarities

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 properties and combines them with m...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Imran N. Junejo, Emilie Dexter, Ivan Laptev, Patrick Pérez
• Keywords: Action Descriptor, Action Sequences, Action Variations, Computer Vision, Cross-view Action Recognition, ECCV 2008, Paper Concerns Recognition