Human Action Recognition using a Hybrid NTLD Classifier

This work proposes a hybrid classifier to recognize human actions in different contexts. In particular, the proposed hybrid classifier (a neural tree with linear discriminant nodes NTLD), is a neural tree whose nodes can be either simple preceptrons or recursive fisher linear discriminant (RFLD) classifiers. A novel technique to substitute bad trained perceptron with more performant linear discriminators is introduced. For a given frame, geometrical features are extracted from the skeleton of the human blob (silhouette). These geometrical features are collected for a fixed number of consecutive frames to recognize the corresponding activity. The resulting feature vector is adopted as input to the NTLD classifier. The performance of the proposed classifier has been evaluated on two available databases.