Paper Info
Title
Improving Slice-Based Model For Person Re-Id With Multi-Level Representation And Triplet-Center Loss
Abstract
Person Re-Identification has received extensive study in the past few years and achieves impressive progress. Recent outstanding methods extract discriminative features by slicing feature maps of deep neural network into several stripes. Still there have improvement on feature fusion and metric learning strategy which can help promote slice-based methods. In this paper, we propose a novel framework that is end-to-end trainable, called Multi-level Slice-based Network (MSN), to capture features both in different levels and body parts. Our model consists of a dual-branch network architecture, one branch for global feature extraction and the other branch for local ones. Both branches process multi-level features using pyramid feature alike module. By concatenating the global and local features, distinctive features are exploited and properly compared. Also, our proposed method creatively introduces a triplet-center loss to elaborate combined loss function, which helps train the joint-learning network. By demonstrating the comprehensive experiments on the mainstream evaluation datasets including Market-1501, DukeMTMC, CUHK03, it indicates that our proposed model robustly achieves excellent performance and outperforms many of existing approaches. For example, on DukeMTMC dataset in single-query mode, we obtain a great result of Rank-1/mAP = 85.9%(+1.0%)/74.2%(+4.7%).
Year
DOI
Venue
2019
10.1587/transinf.2019EDP7067
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
person re-identification, multi-level, body parts, triplet-center loss, combined loss
Computer vision,Pattern recognition,Computer science,Artificial intelligence
Journal
Volume
Issue
ISSN
E102D
11
1745-1361
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Yusheng Zhang100.34
Zhiheng Zhou24323.53
Bo Li317167.08
Yu Huang47423.75
Junchu Huang545.49
Zengqun Chen601.69