Probability
Mixtures of probabilistic principal component analyzers
Neural Computation
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gait Sequence Analysis Using Frieze Patterns
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, we present hybrid motion features to promote action recognition in videos. The features are composed of two complementary components from different views of motion information. On one hand, the period feature is extracted to capture global motion in time-domain. On the other hand, the enhanced histograms of motion words (EHOM) are proposed to describe local motion information. Each word is represented by optical flow of a frame and the correlations between words are encoded into the transition matrix of a Markov process, and then its stationary distribution is extracted as the final EHOM. Compared to traditional Bags of Words representation, EHOM preserves not only relationships between words but also temporary information in videos to some extent. We show that by integrating local and global features, we get improved recognition rates on a variety of standard datasets.