Human centric spatial affordances for improving human activity recognition

Abstract

Spatial affordance can be defined as the functionality a space, or place, lends to human activity. Different places afford different activity possibilities - sleeping is mostly done in the bedroom, and cooking is mostly done in the kitchen. Semantic place labels like kitchen and bedroom, therefore, provide context with which a robot can better infer human activity. Real rooms, however, often defy simple place labels, as they can be multi-purpose, supporting many different types of human activity. The solution is to identify the spatial affordances associated with the current nexus of human activity - a microlevel place labeling. In this paper, we will demonstrate how to estimate these local spatial affordances by integrating a deep learning based place estimator with human pose estimation. The resulting affordances are then used to improve activity recognition using Bayesian belief network.