基于支持样本间接式的行人再识别

孙锐; 方蔚; 黄启恒; 高隽

doi:10.11999/JEIT170215

基于支持样本间接式的行人再识别

doi: 10.11999/JEIT170215

基金项目:

国家自然科学基金(61471154)，安徽省科技攻关科技强警项目(170d0802181)

计量
- 文章访问数: 955
- HTML全文浏览量: 133
- PDF下载量: 281
- 被引次数: 0
出版历程
- 收稿日期: 2017-03-17
- 修回日期: 2017-09-15
- 刊出日期: 2017-12-19

Indirect Person Re-identification Based on Support Samples

Funds:

The National Natural Science Foundation of China (61471154), Anhui Province Science and Technology Research (170d0802181)

摘要

摘要: 行人再识别就是在无重叠视域多摄像机监控系统中，识别出相同的行人。针对来自于不同摄像头行人图片存在着视角、光照和尺度变化的问题。该文提出了基于支持样本间接式匹配的行人再识别方法。该算法首先通过聚类的方法分别提取不同摄像头下的支持样本，当要对来自不同摄像头的行人进行匹配时，在距离测度的基础上利用支持样本分别判别出其所在摄像头下的行人类别，通过类别的对比判断是否为同一行人。该方法避免了不同摄像头下行人图片直接匹配，有效解决不同摄像头带来的视角、光照和尺度问题。实验结果表明该文的算法相比一些经典算法识别率有一定的提高，并且在数据集VIPeR, CAVIAR4ReID和CUHK01上，Rank1分别达到了43.60%, 41.36%, 43.82%。
- 行人再识别 /
- 支持样本 /
- 间接匹配
Abstract: Person re-identification is the identification of the same pedestrian in a multi camera surveillance without overlapping views. Aiming at the problem of the existence of visual angle, illumination and scale change in pedestrian images which from different camera. An indirect person re-identification method is proposed based on the support samples. At first, the algorithm extracts the support samples from different cameras by the clustering method. When it comes to matching pedestrians from different cameras, the support samples are used to distinguish the pedestrians categories under the camera on the basis of the distance metric, by comparing the categories to determine whether the same pedestrian. The method avoids the direct matching of pedestrian images under different cameras, which effectively solve the problem of the existence of visual angle, illumination and scale change in different camera. The experimental results show that the algorithm has a high recognition rate, and on the data set VIPeR, CAVIAR4ReID and CUHK01the, Rank1 reaches 43.60%, 41.36% and 43.82% respectively.
- Person re-identification /
- Support samples /
- Indirect matching

HTML全文

参考文献(31)

ZHAO R, OUYANG W L, WANG X G, et al. Person Re-identification by Salience Matching[C]. IEEE International Conference on Computer Vision, Sydney, Australia, 2013: 2528-2535. doi: 10.1109/ICCV.2013.314.

DORETTO G, SEBATIAN T, TU P, et al. Appearance- based person re-identification in camera networks: Problem overview and current approaches[J]. Journal of Ambient Intelligence and Humanized Computing, 2011, 2(2): 127-151. doi: 10.1007/s12652-010-0034-y.

齐美彬, 顫胜顺, 王运侠, 等. 基于多特征子空间与核学习的行人再识别[J]. 自动化学报, 2016, 42(2): 299-308. doi: 10.16383/i.aas.2016.c150344.

QI Meibin, TAN Shengshun, WANG Yunxia, et al. Multi- feature subspace and kernel learning for person re- identification[J]. Acta Automatica Sinica, 2016, 42(2): 299-308. doi: 10.16383/i.aas.2016.c150344.

ZHAO Rui, OUYANG Wanli, and WANG Xiaogang. Person re-identification by saliency learning[J]. IEEE Transations on Pattern Analysis and Machine Intelligence, 2017, 39(2): 356-370. doi: 10.1109/TPAMI.2016.2544310.

XIAO Tong, LI Hongsheng, OUYANG Wanli, et al. Recurrent convolutional network for vidieo-baesd person re-identification[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, US, 2016: 1325-1334. doi: 10.1109/CVPR.2016.148.

SUN Chong, WANG Dong, and LU Huchaun. Person re-identification via distance metric learning with latent variables[J]. IEEE Transactions on Image Processing, 2017, 26(1): 23-34. doi: 10.1109/TIP.2016.2619261.

SATHISH P K and BALAJI S. Person re-identification using part based hybrid descriptor[C]. 2016 Second International Conference on Cognitive Computing and Information Processing, Mysuru, India, 2016: 1-4. doi: 10.1109/CCIP. 2016.7802849.

GAO Bin, ZENG Mingyong, and XU Shiming. Person re- identtification with discriminatively trained viewpoint invariant orthogonal dictionaries[J]. Electronics Letters, 2016, 52(23): 1914-1916. doi: 10.1049/el.2016.2639.

ZHENG Liang, WANG Shengjin, TIAN Lu, et al. Scalar person re-identification: A benchmark[C]. IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1116-1124. doi: 10.1109/ICCV.2015.133.

WANG Taiqing, GONG Shaogang, ZHU Xiatian, et al. Person re-identification by discriminative selection in video ranking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(12): 2501-2514. doi: 10.1109/ TPAMI.2016.2522418.

GRAY D and TAO H. Viewpoint invariant pedestrian recognition with an ensemble of localized features[C]. European Conference on Computer Vision, Marseille France, 2008: 262-275. doi: 10.1007/978-3-540-88682-2_21.

YANG Y, YANG J, YAN J, et al. Salientcolor names for person re-identification[C]. European Conference on Computer Vision, Zurich, 2014: 536-550. doi: 10.1007/978- 3-319-10590-1_35.

FARENZENA M, BAZZANI L, PERINA A, et al. Person re-identification by symmetry-driven accumulation of local features[C]. 2010 IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA, 2010: 2360-2367. doi: 10.1109/CVPR.2010.5539936.

MA B, SU Y, and JURIE F. Local descriptors encoded by fisher vectors for person re-identification[C]. European Conference on Computer Vision, Firence, Italy, 2012: 413-422. doi: 10.1007/978-3-642-33863-2_41.

LIAO S C, HU Y, ZHU X Y, et al. Person re-identification by local maximal occurrence representation and metric learning [C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 2015: 2197-2206. doi: 10.1109/CVPR.2015. 7298832.

KOSTINGER M, HIRZER M, WOHLHART P, et al. Large scale metric learning from equivalence constraints[C]. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Rhode, Island, 2012: 2288-2295. doi: 10.1109/ CVPR.2012.6247939.

PROSSER B, ZHNEG W S, GONG S, et al. Person reidentification by support vector ranking[C]. British Machine Vision Conference, Aberystwyth, UK, 2010: 21.1-21.11. doi: 10.5244/C.24.21.

TAO D, JIN L, WANG Y, et al. Person reidentification by regularized smoothing kiss metric learning[J]. IEEE Transaction on Circuit and Systems for Video Technology, 2013, 23(10): 1675-1685. doi: 10.1109/ TCSVT.2013.225413.

AHMED E, JONES M, and MARKS T K. An improved deep learning architecture for person re-identification[C]. 2015 IEEE Conference on Computer Visionand Pattern Recognition, Boston, USA, 2015: 3908-3916. doi: 10.1109/ CVPR.2015.7299016.

LI W, ZHAO R, XIAO T, et al. DeepReID: Deep filter pairing neural network for person re-identification[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 152-159. doi: 10.1109/ CVPR.2014.27.

ZHENG Liang, WANG Shenjin, WANG Jingdong, et al. Accurate image search with multi-scalar contextual evidences [J]. International Journal of Computer Vision, 2016, 120(1): 1-13. doi: 10.1007/s11263-016-0889-2.

CHENG D S, CRISTANI M, STOPPA M, et al. Custom pictorial structures for re-identification[C]. British Machine Vision Conference, Scotland, UK, 2011: 68.1-68.11. doi: 10.5244/C.25.68.

LI Z, CHANG S Y, LIANG F, et al. Learning locally adaptive decision functions for person verification[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 3610-3617. doi: 10.1109/CVPR.2013. 463.

ZHAO R, OUYANG W L, and WANG X G. Unsupervised salience learning for person re-identification[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 3586-3593. doi: 10.1109/CVPR.2013. 460.

ZHENG W S, GONG S G, and XIANG T. Person re-identification by probabilistic relative distance comparison [C]. 2011 IEEE Conference on Computer Vision and Pattern Recognition. Conneticut, USA, 2011: 649-656. doi: 10.1109/ CVPR.2011.5995598.

XIAO Tong, LI Hongsheng, OUYANG wanly, et al. Learning deep feature representations with domain guided dropout for person re-identification[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA, 2016: 1249-1258. doi: 10.1109/CVPR.2016. 140.

MIGNON A and JURIE F. PCCA: A new approach for distance learning from sparse pairwise constraints[C]. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Rhode, Island, 2012: 2666-2672. doi: 10.1109/ CVPR.2012.6247987.

PEDAGADI S, ORWELL J, VELASTIN S, et al. Local fisher discriminant analysis for pedestrian Re-identification[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 3318-3325. doi: 10.1109/ CVPR.2013.426.

ZHAO R, OUYANG W L, and WANG X G. Learning mid-level filters for person re-identification[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 144-151. doi: 10.1109/CVPR.2014.26.

XIONG F, GOU M R, CAMPS O, et al. Person re-identification using kernel-based metric learning methods [C]. European Conference on Computer Vision, Zurich, Switzerland, 2014: 1-16. doi: 10.1007/978-3-319- 10584-0_1.

施引文献

资源附件(0)

访问统计