We propose an approach for cross-view action recognition by way of 'virtual views' that connect the action descriptors extracted from one (source) view to those extracted from another (target) view. Each virtual view is associated with a linear transformation of the action descriptor, and the sequence of transformations arising from the sequence of virtual views aims at bridging the source and target views while preserving discrimination among action categories. Our approach is capable of operating without access to labeled action samples in the target view and without access to corresponding action instances in the two views, and it also naturally incorporate and exploit corresponding instances or partial labeling in the target view when they are available. The proposed approach achieves improved or competitive performance relative to existing methods when instance correspondences or target labels are available, and it goes beyond the capabilities of these methods by providing some level of discrimination even when neither correspondences nor target labels exist.
REF learned two view-specific transformations for the source and target views, and then generated a sequence of linear transformations of action descriptors as the virtual views to connect two views.
Discriminative virtual views for cross-view action recognition
2012 IEEE Conference on Computer Vision and Pattern Recognition
2012 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science