Statistics of pairwise co-occurring local spatio-temporal features for human action recognition
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
A line based pose representation for human action recognition
Image Communication
Common-sense reasoning for human action recognition
Pattern Recognition Letters
Graph-based approach for human action recognition using spatio-temporal features
Journal of Visual Communication and Image Representation
| Hi-index | 0.00 |
Existing action recognition approaches mainly rely on the discriminative power of individual local descriptors extracted from spatio-temporal interest points (STIP), while the geometric relationships among the local features are ignored. This paper presents new features, called pairwise features (PWF), which encode both the appearance and the spatio-temporal relations of the local features for action recognition. First STIPs are extracted, then PWFs are constructed by grouping pairs of STIPs which are both close in space and close in time. We propose a combination of two codebooks for video representation. Experiments on two standard human action datasets: the KTH dataset and the Weizmann dataset show that the proposed approach outperforms most existing methods.