Human motion analysis: a review
Computer Vision and Image Understanding
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
International Journal of Computer Vision
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Tracklet descriptors for action modeling and video analysis
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Pairwise Features for Human Action Recognition
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Recognizing Human Actions by Learning and Matching Shape-Motion Prototype Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion history image: its variants and applications
Machine Vision and Applications
Learning neighborhood cooccurrence statistics of sparse features for human activity recognition
AVSS '11 Proceedings of the 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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The bag-of-words approach with local spatio-temporal features have become a popular video representation for action recognition in videos. Together these techniques have demonstrated high recognition results for a number of action classes. Recent approaches have typically focused on capturing global statistics of features. However, existing methods ignore relations between features and thus may not be discriminative enough. Therefore, we propose a novel feature representation which captures statistics of pairwise co-occurring local spatio-temporal features. Our representation captures not only global distribution of features but also focuses on geometric and appearance (both visual and motion) relations among the features. Calculating a set of bag-of-words representations with different geometrical arrangement among the features, we keep an important association between appearance and geometric information. Using two benchmark datasets for human action recognition, we demonstrate that our representation enhances the discriminative power of features and improves action recognition performance.