Part-Pose Guided Amur Tiger Re-Identification

被引：23
作者：
Liu, Cen [1 ]
Zhang, Rong [1 ]
Guo, Lijun [1 ]
机构：
[1] Ningbo Univ, Fac Elect Engn & Comp Sci, Ningbo, Peoples R China
来源：
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW) | 2019年
关键词：
D O I：
10.1109/ICCVW.2019.00042
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we present our solution to tiger reidentification (re-ID) in both the plain and the wild tracks in the 2019 Computer Vision for Wild life Conservation Challenge (CVWC2019). We introduce a novel part-pose guided framework for the tiger re-ID task, which consists of two part streams and one MI stream based on the pose characteristics of tiger. Considering missing and inaccurate pose annotations, the two part streams are used as a regulator to guide the MI stream in learning and aligning the local features in the training stage. We only use the learnt full stream for the tiger re-ID task in the inference stage. The proposed model has the advantage that despite requiring pose information at training time it is not needed during inference, so it is particularly suitable for tiger re-ID in the wild. Our proposed method outperforms the state-of-the-art and finished top in both the PlainID and WildID competitions at CVWC2019. The source of code will be public available at https: //githut.com/LcenArthas/CVWC2019-Amur-Tiger-Re-ID
引用
收藏
页码：315 / 322
页数：8
相关论文
共 40 条
[1]  
[Anonymous], 2017, 31 AAAI C ART INT
[2]  
Bouma S., 2018, 2018 INT C IM VIS CO, P1
[3]  
Bouma S, 2018, INT CONF IMAG VIS
[4]  
Branson S., 2014, BRITISH MACHINE VISI
[5]  
Branson Steve, 2014, BRIT MACH VIS C
[6]  
Chen W., 2017, THIRTY FIRST AAAI CO
[7]   Person Re-Identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function [J].
Cheng, De ;
Gong, Yihong ;
Zhou, Sanping ;
Wang, Jinjun ;
Zheng, Nanning .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :1335-1344
[8]   Deep feature learning with relative distance comparison for person re-identification [J].
Ding, Shengyong ;
Lin, Liang ;
Wang, Guangrun ;
Chao, Hongyang .
PATTERN RECOGNITION, 2015, 48 (10) :2993-3003
[9]   Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels [J].
Han, Bo ;
Yao, Quanming ;
Yu, Xingrui ;
Niu, Gang ;
Xu, Miao ;
Hu, Weihua ;
Tsang, Ivor W. ;
Sugiyama, Masashi .
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018, 31
[10]  
He K., 2015, IEEE I CONF COMP VIS, P1026, DOI DOI 10.1109/ICCV.2015.123
1234