Stats-Calculus Pose Descriptor Feeding A Discrete HMM Low-latency Detection and Recognition System For 3D Skeletal Actions
This addresses the need for efficient action recognition in applications requiring real-time processing, but appears incremental as it builds on existing Moving Pose descriptors.
The paper tackles the problem of low-latency recognition of 3D skeletal human actions by presenting a system that uses Hidden Markov Models with seven novel pose descriptors based on a Stats-Calculus feature extraction technique, achieving unspecified results without concrete numbers.
Recognition of human actions, under low observational latency, is a growing interest topic, nowadays. Many approaches have been represented based on a provided set of 3D Cartesian coordinates system originated at a certain specific point located on a root joint. In this paper, We will present a statistical detection and recognition system using Hidden Markov Model using 7 types of pose descriptors. * Cartesian Calculus Pose descriptor. * Angular Calculus Pose descriptor. * Mixed-mode Stats-Calculus Pose descriptor. * Centro-Stats-Calculus Pose descriptor. * Rela-Centro-Stats-Calculus Pose descriptor. * Rela-Centro-Stats-Calculus DCT Pose descriptor. * Rela-Centro-Stats-Calculus DCT-AMDF Pose descriptor. Stats-Calculus is a feature extracting technique, that is developed on Moving Pose descriptor , but using a combination of Statistics measures and Calculus measures.