Human Activity Recognition from Skeletal Data using Covariance Descriptor and Temporal Subspace Clustering

Human Activity Recognition (HAR) is one of the most active research areas in fields of computer vision and pattern analysis. Most of the existing HAR algorithms are devised in supervised manner by excluding the temporal aspects of skeletal data which is a key parameter in HAR. Motivated by this, we have designed and developed an efficient subspace clustering algorithm for HAR by explicitly considering the time series aspects of human activity data. Designing this algorithm in an unsupervised manner is another challenge that we are dealing with. The work involves design of an efficient covariance descriptor for encoding the skeletal data. Later a subspace clustering algorithm called temporal subspace clustering (TSC) algorithm is designed by exploiting the principles of Laplacian regularization and dictionary learning. Experimental analysis shows that the proposed method outperforms the state-of-the-art methods employed for HAR.