UGraSP: A unified framework for activity recognition and person identification using graph signal processing
With the growing availability and wide distribution of low-cost, high-performance 3D imaging sensors, the image analysis community has witnessed an increased demand for solutions to the challenges of activity recognition and person identification. We propose an integrated framework, based on graph signal processing, that simultaneously performs both tasks using a single set of features. The novelty of our approach is based on the fact that the set of features used for activity recognition accommodates person identification without additional computation. The analysis is based on the extracted structure-invariant graph (skeleton). The Laplacian of the skeleton is used both to identify the person and recognize the performed activity. While person identification is achieved directly from the analysis of the Laplacian, activity recognition is obtained after transformation, into the graph spectral domain, of the vectorized form of the skeletal joints 3D coordinates. Feature vectors for activity recognition are then derived, in this domain, from the covariance matrices evaluated over fixed-length sequential video segments. Both classification tasks are implemented using linear support vector machines (SVM). When applied to real activity datasets, our approach shows an improved performance over the existing state-of-the-art.