The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Appearance-Based Approach to Gesture-Recognition
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume II
Probabilistic Motion Parameter Models for Human Activity Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Space-Time Behavior Based Correlation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Action recognition with global features
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
"Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting
IEEE Transactions on Fuzzy Systems
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In this study, a new method for recognizing everyday life actions is proposed. To enhance robustness, each sequence is characterized globally. Detection of moving areas is first performed on each image. All binary points form a volume in the three-dimensional (3D) space (x,y,t). This volume is characterized by its geometric 3D moments which are used to form a feature vector for the recognition. Action recognition is then carried out by employing two classifiers independently: a) a nearest center classifier, and b) an auto-associative neural network. The performance of these two is examined, separately. Based on this evaluation, these two classifiers are combined. For this purpose, a relevancy matrix is used to select between the results of the two classifiers, on a case by case basis. To validate the suggested approach, results are presented and compared to those obtained by using only one classifier.