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.

中文翻译：

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E$^3$Pose：用于多人 3D 姿态估计的节能边缘辅助多摄像头系统

多人 3D 姿势估计在建立现实世界和虚拟世界之间的无缝连接方面起着关键作用。最近的努力采用了一个两阶段框架，首先从不同的角度在多个摄像机视图中建立 2D 姿态估计，然后将它们合成为 3D 姿态。然而，重点主要是在离线视频数据集上开发新的计算机视觉算法，而没有过多考虑具有灵活部署和电池供电相机的现实世界系统中的能量限制。在本文中，我们基于自适应相机选择的关键思想，提出了一种节能的边缘辅助多相机系统，称为 E$^3$Pose，用于实时多人 3D 姿势估计。而不是像现有作品那样总是使用所有可用的相机来执行 2D 姿态估计，E$^3$Pose 以自适应方式根据遮挡和能量状态方面的相机视图质量仅选择一部分相机，从而降低能耗（这转化为延长电池寿命）并提高估计精度。为了实现这一目标，E$^3$Pose 结合了基于注意力的 LSTM 来预测每个摄像机视图的遮挡信息，并在选择摄像机处理场景图像之前指导摄像机选择，并运行基于Lyapunov 优化框架做出长期自适应选择决策。我们在 5 摄像头测试台上构建了 E$^3$Pose 原型，证明了其可行性并评估了其性能。我们的结果表明，显着节能（高达 31.