Embodied Human Activity Recognition

Sha Hu, Yu Gong, Greg Mori

Abstract: We study how to utilize the mobility of an embodied agent to improve its ability to recognize human activities. We introduce the embodied human activity recognition problem, where an agent moves in a 3D environment to recognize the category of ongoing human activities. The agent must make movement decisions based on its egocentric observations acquired up to the current time, with the goal of choosing movements to obtain new views that lead to accurate human activity recognition. Towards this goal, we propose a reinforcement learning approach that learns a policy controlling the agent’s movements over time. We evaluate our approach with two realistic human activity datasets. Results show that our approach can learn to move effectively to achieve high performance in recognizing human activities.