Cross-Modal Person Re-identification Based on Local Heterogeneous Collaborative Dual-Path Network

被引：0
作者：
Zheng A. [1 ,2 ]
Zeng X. [1 ]
Jiang B. [1 ,2 ]
Huang Y. [3 ]
Tang J. [1 ,2 ]
机构：
[1] Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei
[2] Key Laboratory of Industrial Image Processing and Analysis of Anhui Province, Science and Technology Department of Anhui Province, Hefei
[3] National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing
来源：
Jiang, Bo (jiangbo@ahu.edu.cn) | 1600年 / Science Press卷 / 33期
基金：
中国国家自然科学基金;
关键词：
Collaborative Fusion; Convolutional Neural Network; Cross-Modal; Local Feature; Person Re-identification;
D O I：
10.16451/j.cnki.issn1003-6059.202010001
中图分类号：
学科分类号：
摘要：
The coordinating fusion between modalities is ignored in the existing cross-modal person re-identification methods in the learning process. In this paper, a strategy for cross-modal person re-identification(Re-ID) based on local heterogeneous collaborative dual-path network is proposed. Firstly, the global features of each modality are extracted by the dual-path network for local refinement, and the structured local information of pedestrians is mined. Then, the local information of different modalities is correlated with the label and prediction information to achieve cooperative adaptive fusion and learn more discriminative features. The effectiveness of the proposed method is demonstrated through comprehensive experiments on two benchmarks RegDB and SYSU-MM01. © 2020, Science Press. All right reserved.
引用
收藏
页码：867 / 878
页数：11
相关论文
共 54 条
  • [1] LAN X Y, MA A J, YUEN P C, Et al., Joint Sparse Representation and Robust Feature-Level Fusion for Multi-cue Visual Tracking, IEEE Transactions on Image Processing, 24, 12, pp. 5826-5841, (2015)
  • [2] CHANG X J, HUANG P Y, SHEN Y D, Et al., RCAA: Relational Context-Aware Agents for Person Search, Proc of the European Conference on Computer Vision, pp. 86-102, (2018)
  • [3] FARENZENA M, BAZZANI L, PERINA A, Et al., Person Re-identification by Symmetry-Driven Accumulation of Local Features, Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2360-2367, (2010)
  • [4] GRAY D, TAO H., Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features, Proc of the European Confe-rence on Computer Vision, pp. 262-275, (2008)
  • [5] KVIATKOVSKY I, ADAM A, RIVLIN E., Color Invariants for Person Re-identification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 7, pp. 1622-1634, (2013)
  • [6] LIAO S C, HU Y, ZHU X Y, Et al., Person Re-identification by Local Maximal Occurrence Representation and Metric Learning, Proc of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2197-2206, (2015)
  • [7] MA B P, SU Y, JURIE F., Local Descriptors Encoded by Fisher Vectors for Person Re-identification, Proc of the European Confe-rence on Computer Vision, pp. 413-422, (2012)
  • [8] MATSUKAWA T, OKABE T, SUZUKI E, Et al., Hierarchical Gaussian Descriptor for Person Re-identification, Proc of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1363-1372, (2016)
  • [9] ZHAO R, OUYANG W L, WANG X G., Learning Mid-level Filters for Person Re-identification, Proc of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 144-151, (2014)
  • [10] LI W, ZHAO R, XIAO T, Et al., DeepReID: Deep Filter Pairing Neural Network for Person Re-identification, Proc of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 152-159, (2014)
123456