Slow Feature Subspace for Action Recognition

This paper proposes a framework for human action recognition using a combination of subspace-based methods and slow feature analysis (SFA). Subspace-based methods can compactly model the distribution of multiple images from a video by a low dimensional subspace even when few data is available. However, the temporal information of the video is lost after generating the subspace using principal component analysis (PCA). In contrast, PCA-SFA, which is a variant of SFA, can produce a valid video descriptor as a basis of a slow feature space from a given image sequence. In the proposed framework, we extract a valid video descriptor from an input video by conducting PCA-SFA, and then transform the descriptor into a subspace by using PCA. This new representation of slow feature subspace includes temporal dynamic information. Thus, we can compare two sequences and perform classification by simply calculating the similarity between their slow feature subspaces. The effectiveness of our framework is demonstrated through extensive experiments with two publicly available datasets, KTH action and the Chinese sign language dataset (isolated SLR500).