A Model for Luggage Re-identification Based on the Network Ensemble
Abstract
Luggage re-identification based on the image recognition is a highly demanding task for security check. The final result of testing model of using single network and loss function is under our expectation. Therefore, network ensemble which integrate multiple networks and loss ensemble which adopt various loss functions are explored and adopted in this paper. Meantime, most of technology of re-identification are adopted in this paper. In order to improve generalization ability, random erasing, batch feature erasing, random flip are adopted in this paper. Multibranch structure is adopted to extract more features and re-ranking is adopted to improve final accuracy. These methods are integrated into the model of luggage re-identification. Experimental results show that the better accuracy is obtained by our method. The model has strong value of practical application.
References
[1]
Hao Luo, Youzhi Gu, Xingyu Liao, Shenqi Lai, Wei Jiang. Bag of Tricks and A Strong Baseline for Deep Person Re-identification. arXiv: 1903.07071, 2019. 5
[2]
Z. Zhong, L. Zheng, D. Cao, and S. Li. Re-ranking person re-identification with k-reciprocal encoding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1318--1327, 2017. 7
[3]
Z. Dai, M. Chen, S. Zhu, and P. Tan. Batch feature erasing for person re-identification and beyond. arXiv: 1811.07130, 2018.7
[4]
A. Hermans, L. Beyer, and B. Leibe. In defense of the triplet loss for person re-identification. arXiv: 1703.07737, 2017. 2
[5]
Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang. Random erasing data augmentation. arXiv: 1708.04896, 2017. 3
[6]
Yutian Li, Feng Gao, Zhijian Ou, Jiasong Sun. Angular Softmax Loss for End-to-end Speaker Verification. arXiv:1806.03464, 2018.
[7]
Lei Qi, Jing Huo, Lei Wang, Yinghuan Shi, and Yang Gao. Maskreid: A mask based deep ranking neural network for person re-identivcation. arXiv: 1804.03864, 2018. 4
[8]
XuanZhang, HaoLuo, XingFan, WeilaiXiang, YixiaoSun, Qiqi Xiao, Wei Jiang, Chi Zhang, and Jian Sun. Alignedreid: Surpassing human-level performance in person reidentification. arXiv: 1711.08184, 2017. 11
[9]
Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Enhua Wu. Squeeze-and-Excitation Networks. arXiv:1709.01507, 2017. 9
[10]
Liang Zheng, Yujia Huang, Huchuan Lu, and Yi Yang. Pose invariant embedding for deep person re-identification. arXiv:1701.07732, 2017. 1
[11]
Zhun Zhong, Liang Zheng, Guoliang Kang, and YiYang. Random erasing data augmentation. arXiv: 1708.04896, 2017. 8
[12]
Xingang Pan, Ping Luo, Jianping Shi, Xiaoou Tang. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net. arXiv:1807.09441, 2020.3
[13]
K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016. 5
[14]
R. Zhao, W. Ouyang, and X. Wang. Unsupervised salience learning for person re-identification. In CVPR, 2013. 6
[15]
C. Su, S. Zhang, J. Xing, W. Gao, and Q. Tian. Deep attributes driven multi-camera person re-identification. In ECCV, 2016. 6
[16]
S. Karanam, M. Gou, Z. Wu, A. Rates-Borras, O. Camps, and R. J. Radke. A comprehensive evaluation and benchmark for person re-identification: Features, metrics, and datasets. arXiv preprint arXiv: 1605.09653, 2016. 6
[17]
L. Zhang, T. Xiang, and S. Gong. Learning a discriminative null space for person re-identification. In CVPR, 2016. 2, 6
[18]
R. Zhao, W. Ouyang, and X. Wang. Unsupervised salience learning for person re-identification. In CVPR, 2013. 6
[19]
C. Su, S. Zhang, J. Xing, W. Gao, and Q. Tian. Deep attributes driven multi-camera person re-identification. In ECCV, 2016. 6
[20]
V.-H. Nguyen, T. D. Ngo, K. M. Nguyen, D. A. Duong, K. Nguyen, and D.-D. Le. Re-ranking for person re-identification. In SoCPaR. IEEE, 2013. 2
Index Terms
- A Model for Luggage Re-identification Based on the Network Ensemble
Recommendations
TRINet: Tracking and Re-identification Network for Multiple Targets in Egocentric Videos Using LSTMs
Computer Analysis of Images and PatternsAbstractWe present a recurrent network based novel framework for tracking and re-identifying multiple targets in first-person perspective. Even though LSTMs can act as a sequence classifier, most of the previous works in multi target tracking use their ...
Quality Prediction Model Based on Novel Elman Neural Network Ensemble
In this paper, we propose a novel prediction algorithm based on an improved Elman neural network (NN) ensemble for quality prediction, thus achieving the quality control of designed products at the product design stage. First, the Elman NN parameters are ...
A unified evolutionary training scheme for single and ensemble of feedforward neural network
Artificial neural networks (ANNs) have been successfully applied to many areas. The key of success is to properly tune the architecture and the connection weights of the ANN and to have the trained ANN own higher generalization ability. For complicated ...
Comments
Information & Contributors
Information
Published In
October 2020
552 pages
ISBN:9781450387835
DOI:10.1145/3436369
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- Beijing University of Technology
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 January 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICCPR 2020
ICCPR 2020: 2020 9th International Conference on Computing and Pattern Recognition
October 30 - November 1, 2020
Xiamen, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 41Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)0
Reflects downloads up to 28 Feb 2025
Other Metrics
Citations
Cited By
View allView Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in