Real-Time Human Action Recognition System Using Depth Map Sequences

Abstract

This work presents a real-time human action recognition system that uses depth map sequence as input. The system contains the segmentation of human, the action modeling based on 3D shape context and the action graph algorithm. We effectively solve the problem of segmenting human from complex and cluttered scenes by combing a novel quadtree split-and-merge method and the codebook background modeling algorithm. We aims at recognizing actions that are used in games and interactions, especially complex actions that contain foot motion and body heave. By expanding the shape context descriptor into 3D space, we obtain translation and scale invariant features and get rid of normalization error, which is a common problem of real-life applications. Experiments in various scenarios demonstrate the high speed and excellent performance of our procedure.