A Novel Human Action Recognition Algorithm Based on Decision Level Multi-Feature Fusion

In order to take advantage of the logical structure of video sequences and improve the recognition accuracy of the human action, a novel hybrid human action detection method based on three descriptors and decision level fusion is proposed. Firstly, the minimal 3D space region of human action region is detected by combining frame difference method and ViBE algorithm, and the three-dimensional histogram of oriented gradient (HOG3D) is extracted. At the same time, the characteristics of global descriptors based on frequency domain filtering (FDF) and the local descriptors based on spatial-temporal interest points (STIP) are extracted. Principal component analysis (PCA) is implemented to reduce the dimension of the gradient histogram and the global descriptor, and bag of words (BoW) model is applied to describe the local descriptors based on STIP. Finally, a linear support vector machine (SVM) is used to create a new decision level fusion classifier. Some experiments are done to verify the performance of the multi-features, and the results show that they have good representation ability and generalization ability. Otherwise; the proposed scheme obtains very competitive results on the well-known datasets in terms of mean average precision.