Publication

Abstract : In this paper, a new method for the efficient clustering of human activity data is proposed. Unlike the traditional human activity clustering approaches, our method relies on the skeletal data recorded with the help of motion capture (mocap) systems to achieve the goal. The proposed method is structured around the kernel-based robust covariance descriptor. By introducing a data re-framing technique that efficiently utilizes the temporal properties of the human activity data, we have alleviated the data redundancy and insufficiency issues associated with action sequences. The optimization model developed encompasses the combined benefits of low-rank representation and least square regression. The formulation is strengthened by incorporating the temporal dependency of the human activity sequences with the help of a temporal Laplacian regularizer. With the proposed algorithm, a representation matrix is learned from the raw data, which is then used to perform subspace clustering. Experiments conducted on multiple human activity datasets reveal the ability of the proposed method to achieve better clustering results compared to state-of-the-art counterparts.