Description

ABSTRACT:

     Supervised manifold learning has been successfully applied to action recognition, in which

class label information could improve recognition performance. However, the learned manifold

may not be able to well preserve both the local structure and global constraint of temporal

labels in action sequences. To overcome this problem, we propose Gray level Co occurrence

matrix for action classification. Initially temporal pose correspondence (TPC) between

sequences of the same action are identified. The measurement of the pose image is taken as the

Feature values. Then we extract the GLCM from the each sequence in the action video. Finally

the extracted feature is classified by the Adaptive Neuro Fuzzy Inference System Classifier. The

trained classifier will predict the about input video.