Adaptive human-centered representation for activity recognition of multiple individuals from 3D point cloud sequences

Abstract

Activity recognition of multi-individuals (ARMI) within a group, which is essential to practical human-centered robotics applications such as childhood education, is a particularly challenging and previously not well studied problem. We present a novel adaptive human-centered (AdHuC) representation based on local spatio-temporal features (LST) to address ARMI in a sequence of 3D point clouds. Our human-centered detector constructs affiliation regions to associate LST features with humans by mining depth data and using a cascade of rejectors to localize humans in 3D space. Then, features are detected within each affiliation region, which avoids extracting irrelevant features from dynamic background clutter and addresses moving cameras on mobile robots. Our feature descriptor is able to adapt its support region to linear perspective view variations and encode multi-channel information (i.e., color and depth) to construct the final representation. Empirical studies validate that the AdHuC representation obtains promising performance on ARMI using a Meka humanoid robot to play multi-people Simon Says games. Experiments on benchmark datasets further demonstrate that our adaptive human-centered representation outperforms previous approaches for activity recognition from color-depth data.