多设备边缘网络中用于实时3D人体姿态估计的协作推理
Cooperative Inference for Real-Time 3D Human Pose Estimation in Multi-Device Edge Networks
AI总结：
针对资源受限环境下实时3D人体姿态估计的挑战，提出一种MEC网络中的协作推理方法，通过双重置信度阈值过滤模糊图像并卸载至边缘服务器重评估，构建联合优化问题最小化MPJPE并满足延迟约束，实验证明其能显著降低误差。
摘要：
由于计算复杂度高，在资源受限和动态环境中实现准确且实时的三维（3D）姿态估计具有挑战性。为解决此问题，本研究提出了一种用于移动边缘计算（MEC）网络中实时3D人体姿态估计的新型协作推理方法。在所提出的方法中，配备轻量级推理模型的多个终端设备采用双重置信度阈值来过滤模糊图像。仅过滤后的图像被卸载到具有更强大推理模型的边缘服务器进行重新评估，从而在计算和通信约束下提高估计精度。我们数值分析了所提出推理方法在推理精度和端到端延迟方面的性能，并构建了一个联合优化问题，以推导每个设备的最佳置信度阈值和传输时间，目标是在满足所需端到端延迟约束的同时，最小化平均每关节位置误差（MPJPE）。为解决此问题，我们证明了最小化MPJPE等价于最大化所有设备的推理精度之和，将问题分解为可处理的子问题，并提出了一种低复杂度优化算法以获得近似最优解。实验结果表明，根据置信度阈值的不同，MPJPE与端到端延迟之间存在权衡关系。此外，结果证实，所提出的协作推理方法通过最佳选择置信度阈值和传输时间，实现了MPJPE的显著降低，同时在各种MEC环境中始终满足端到端延迟要求。
英文摘要：
Accurate and real-time three-dimensional (3D) pose estimation is challenging in resource-constrained and dynamic environments owing to its high computational complexity. To address this issue, this study proposes a novel cooperative inference method for real-time 3D human pose estimation in mobile edge computing (MEC) networks. In the proposed method, multiple end devices equipped with lightweight inference models employ dual confidence thresholds to filter ambiguous images. Only the filtered images are offloaded to an edge server with a more powerful inference model for re-evaluation, thereby improving the estimation accuracy under computational and communication constraints. We numerically analyze the performance of the proposed inference method in terms of the inference accuracy and end-to-end delay and formulate a joint optimization problem to derive the optimal confidence thresholds and transmission time for each device, with the objective of minimizing the mean per-joint position error (MPJPE) while satisfying the required end-to-end delay constraint. To solve this problem, we demonstrate that minimizing the MPJPE is equivalent to maximizing the sum of the inference accuracies for all devices, decompose the problem into manageable subproblems, and present a low-complexity optimization algorithm to obtain a near-optimal solution. The experimental results show that a trade-off exists between the MPJPE and end-to-end delay depending on the confidence thresholds. Furthermore, the results confirm that the proposed cooperative inference method achieves a significant reduction in the MPJPE through the optimal selection of confidence thresholds and transmission times, while consistently satisfying the end-to-end delay requirement in various MEC environments.
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