论文标题
视野意识到的无监督车辆重新识别的渐进聚类
Viewpoint-aware Progressive Clustering for Unsupervised Vehicle Re-identification
论文作者
论文摘要
车辆重新识别（RE-ID）是一项积极的任务，因为它在智能城市的大规模智能监控中的重要性。尽管近年来取得的进展很快，但大多数现有方法都以监督的方式处理了车辆重新ID任务，这既是时间又耗费劳动力，并将其应用于现实生活中的情况。最近，无监督的人重新ID方法通过探索域的适应或基于聚类的技术来实现令人印象深刻的性能。但是，由于车辆图像在不同的角度显示出巨大的外观变化，因此无法将这些方法直接概括为车辆重新ID。为了解决这个问题，我们为无监督的车辆重新ID提出了一种新颖的观点感知聚类算法。特别是，我们首先根据预测的观点将整个特征空间划分为不同的子空间，然后执行渐进聚类以挖掘样品之间的准确关系。针对两个多观看基准数据集和Veri-Wild上的最新方法的全面实验验证了在处理无域名的车辆重新ID的同时，在有和没有域名适应方案的情况下，提出的方法的有希望的性能。
Vehicle re-identification (Re-ID) is an active task due to its importance in large-scale intelligent monitoring in smart cities. Despite the rapid progress in recent years, most existing methods handle vehicle Re-ID task in a supervised manner, which is both time and labor-consuming and limits their application to real-life scenarios. Recently, unsupervised person Re-ID methods achieve impressive performance by exploring domain adaption or clustering-based techniques. However, one cannot directly generalize these methods to vehicle Re-ID since vehicle images present huge appearance variations in different viewpoints. To handle this problem, we propose a novel viewpoint-aware clustering algorithm for unsupervised vehicle Re-ID. In particular, we first divide the entire feature space into different subspaces according to the predicted viewpoints and then perform a progressive clustering to mine the accurate relationship among samples. Comprehensive experiments against the state-of-the-art methods on two multi-viewpoint benchmark datasets VeRi and VeRi-Wild validate the promising performance of the proposed method in both with and without domain adaption scenarios while handling unsupervised vehicle Re-ID.
