论文标题
属性引导的特征提取和增强功能可靠的车辆重新识别
Attribute-guided Feature Extraction and Augmentation Robust Learning for Vehicle Re-identification
论文作者
论文摘要
车辆重新识别是智能运输系统和智能城市的核心技术之一，但是较大的阶层内多样性和类间相似性为现有方法带来了巨大的挑战。在本文中，我们提出了一种多引导的学习方法，该方法利用属性信息，同时引入了两个新颖的随机增强，以改善训练期间的鲁棒性。更重要的是，我们提出了一种属性约束方法和组重新排列策略，以完善匹配结果。我们的方法在CVPR 2020 AI City Challenge中获得了66.83％的地图和排名1的准确性76.05％。
Vehicle re-identification is one of the core technologies of intelligent transportation systems and smart cities, but large intra-class diversity and inter-class similarity poses great challenges for existing method. In this paper, we propose a multi-guided learning approach which utilizing the information of attributes and meanwhile introducing two novel random augments to improve the robustness during training. What's more, we propose an attribute constraint method and group re-ranking strategy to refine matching results. Our method achieves mAP of 66.83% and rank-1 accuracy 76.05% in the CVPR 2020 AI City Challenge.
