Learning Incremental Triplet Margin for Person Re-identification

Autor:	Zhang, Yingying, Zhong, Qiaoyong, Ma, Liang, Xie, Di, Pu, Shiliang
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Person re-identification (ReID) aims to match people across multiple non-overlapping video cameras deployed at different locations. To address this challenging problem, many metric learning approaches have been proposed, among which triplet loss is one of the state-of-the-arts. In this work, we explore the margin between positive and negative pairs of triplets and prove that large margin is beneficial. In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively. Multiple levels of feature maps are exploited to make the learned features more discriminative. Besides, we introduce global hard identity searching method to sample hard identities when generating a training batch. Extensive experiments on Market-1501, CUHK03, and DukeMTMCreID show that our approach yields a performance boost and outperforms most existing state-of-the-art methods. Comment: accepted by AAAI19 as spotlight
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1812.06576 Zobrazit plný text záznamu View this record from Arxiv