Recent works in the person re-identification task mainly focus on the model accuracy while ignoring factors related to efficiency, e.g., model size and latency, which are critical for practical application. In this paper, we propose a novel Hierarchical and Efficient Network (HENet) that learns hierarchical global, partial, and recovery features ensemble under the supervision of multiple loss combinations. To further improve the robustness against the irregular occlusion, we propose a new dataset augmentation approach, dubbed random polygon erasing, to random erase the input image’s irregular area imitating the body part missing. We also propose an Efficiency Score (ES) metric to evaluate the model efficiency. Extensive experiments on Market1501, DukeMTMC-ReID, and CUHK03 datasets show the efficiency and superiority of our approach compared with epoch-making methods. We further deploy HENet on a robotic car, and the experimental result demonstrates the effectiveness of our method for robotic navigation.

人员重新识别任务的最新工作主要集中在模型准确性上，而忽略了与效率相关的因素，例如模型大小和等待时间，这对于实际应用至关重要。在本文中，我们提出了一个新颖的^ h ierarchical和 Ë fficient 网工作（HENet）获悉该分级全局，局部和恢复功能的多种组合损失的监督下合奏。为了进一步提高针对不规则遮挡的鲁棒性，我们提出了一种新的数据集扩充方法，称为随机多边形擦除，以随机擦除模仿身体部位缺失的输入图像的不规则区域。我们还提出了一种Ë fficiency 小号核心（ES）指标来评估模型效率。在Market1501，DukeMTMC-ReID和CUHK03数据集上进行的大量实验表明，与划时代的方法相比，我们的方法具有更高的效率和优越性。我们将HENet进一步部署在机器人汽车上，实验结果证明了我们的机器人导航方法的有效性。