The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Informative Shape Representations for Human Action Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human action recognition using boosted EigenActions
Image and Vision Computing
A survey on vision-based human action recognition
Image and Vision Computing
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This paper proposes a new method of semi-supervised human action recognition. In our approach, the motion energy image(MEI) and motion history image(MHI) are firstly used as the feature representation of the human action. Then, the constrained semi-supervised kmeans clustering algorithm is utilized to predict the class label of unlabeled training example. Meanwhile the average motion energy and history images are calculated as the recognition model for each category action. The category of the observed action is determined according to the correlation coefficients between its feature images and the pre-established average templates. The experiments on Weizmann dataset demonstrate that our method is effective and the average recognition accuracy can reach above 90% even when only using very small number of labeled action sequences.