Person re-identification plays a key role in applications where a mobile robot needs to track its users over a long period of time, even if they are partially unobserved for some time, in order to follow them or be available on demand. In this context, deep-learning based real-time feature extraction on a mobile robot is often performed on special-purpose devices whose computational resources are shared for multiple tasks. Therefore, the inference speed has to be taken into account. In contrast, person re-identification is often improved by architectural changes that come at the cost of significantly slowing down inference. Attention blocks are one such example. We will show that some well-performing attention blocks used in the state of the art are subject to inference costs that are far too high to justify their use for mobile robotic applications. As a consequence, we propose an attention block that only slightly affects the inference speed while keeping up with much deeper networks or more complex attention blocks in terms of re-identification accuracy. We perform extensive neural architecture search to derive rules at which locations this attention block should be integrated into the architecture in order to achieve the best trade-off between speed and accuracy. Finally, we confirm that the best performing configuration on a re-identification benchmark also performs well on an indoor robotic dataset.

翻译：行人重识别在移动机器人需要长时间跟踪用户、即使目标部分时间段未观测也能持续追踪或按需响应的应用中发挥着关键作用。在此背景下，移动机器人上基于深度学习的实时特征提取通常在与多任务共享计算资源的专用设备上执行，因此推理速度必须纳入考量。与之相对，行人重识别常通过架构改进来提升性能，但这类改进往往以显著降低推理速度为代价。注意力块便是其中一例。我们将证明，当前最先进方法中使用的若干高性能注意力块，其推理开销过高，难以证明其在移动机器人应用中的合理性。为此，我们提出一种注意力块，在保持与更深层网络或更复杂注意力块相当的重识别精度前提下，仅对推理速度产生轻微影响。我们通过大规模神经架构搜索推导出该注意力块在架构中应插入的最佳位置，以实现速度与精度的最优权衡。最后，我们在重识别基准测试上确认最佳配置，在室内机器人数据集上同样表现优异。