Viewpoint-aware Progressive Clustering for Unsupervised Vehicle Re-identification

• URL: http://arxiv.org/abs/2011.09099v1
• Date: Wed, 18 Nov 2020 05:40:14 GMT
• Title: Viewpoint-aware Progressive Clustering for Unsupervised Vehicle Re-identification
• Authors: Aihua Zheng, Xia Sun, Chenglong Li, Jin Tang
• Abstract summary: 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.
• Score: 36.60241974421236
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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