Abstract
Multi-human 3D pose estimation plays a key role in establishing a seamless
connection between the real world and the virtual world. Recent efforts adopted
a two-stage framework that first builds 2D pose estimations in multiple camera
views from different perspectives and then synthesizes them into 3D poses.
However, the focus has largely been on developing new computer vision
algorithms on the offline video datasets without much consideration on the
energy constraints in real-world systems with flexibly-deployed and
battery-powered cameras. In this paper, we propose an energy-efficient
edge-assisted multiple-camera system, dubbed E$^3$Pose, for real-time
multi-human 3D pose estimation, based on the key idea of adaptive camera
selection. Instead of always employing all available cameras to perform 2D pose
estimations as in the existing works, E$^3$Pose selects only a subset of
cameras depending on their camera view qualities in terms of occlusion and
energy states in an adaptive manner, thereby reducing the energy consumption
(which translates to extended battery lifetime) and improving the estimation
accuracy. To achieve this goal, E$^3$Pose incorporates an attention-based LSTM
to predict the occlusion information of each camera view and guide camera
selection before cameras are selected to process the images of a scene, and
runs a camera selection algorithm based on the Lyapunov optimization framework
to make long-term adaptive selection decisions. We build a prototype of
E$^3$Pose on a 5-camera testbed, demonstrate its feasibility and evaluate its
performance. Our results show that a significant energy saving (up to 31.21%)
can be achieved while maintaining a high 3D pose estimation accuracy comparable
to state-of-the-art methods.