Temporal Subspace Clustering for Unsupervised Action Segmentation

Action segmentation (segmenting a continuous sequence of motion data into a set of actions) has a wide range of applications and plays a role in many problems in computer vision. We look at subspace clustering as an unsupervised approach for this task. Classical subspace clustering methods uncover relationships within the data by learning codes for the samples (i.e. frames), but in this process these methods do not consider the temporal dependency of nearby samples in motion data. In this paper, we propose two subspace clustering methods with temporal regularization terms: Temporal Sparse Subspace Clustering Laplacian Regularization (TSSC-LR) and Temporal Sparse Subspace Clustering Linear Combination (TSSC-LC). TSSC-LR encourages similar codings of samples within a window and TSSC-LC enforces codings within a window to be linear combinations of each other. Efficient ADMM algorithms are proposed for each method. Experiments against state-of-the-art methods on three action datasets demonstrate the effectiveness of the two proposed methods.