激光与光电子学进展, 2019, 56 (14): 141003, 网络出版: 2019-07-12   

基于深度学习行人属性自适应权重分配行人再识别方法 下载: 1087次

Pedestrian Re-Identification Based on Adaptive Weight Assignment using Deep Learning for Pedestrian Attributes
李净 1管业鹏 1,2,*
作者单位
1 上海大学通信与信息工程学院, 上海 200444
2 上海大学新型显示技术及应用集成教育部重点实验室, 上海 200072
引用该论文

李净, 管业鹏. 基于深度学习行人属性自适应权重分配行人再识别方法[J]. 激光与光电子学进展, 2019, 56(14): 141003.

Li Jing, Yepeng Guan. Pedestrian Re-Identification Based on Adaptive Weight Assignment using Deep Learning for Pedestrian Attributes[J]. Laser & Optoelectronics Progress, 2019, 56(14): 141003.

参考文献

[1] ZhaoR, Ouyang WL, Wang XG. Person re-identification by salience matching[C]∥2013 IEEE International Conference on Computer Vision, December 1-8, 2013, Sydney, NSW, Australia. New York: IEEE, 2013: 2528- 2535.

[2] 朱小波, 车进. 基于特征融合与子空间学习的行人重识别算法[J]. 激光与光电子学进展, 2019, 56(2): 021503.

    Zhu X B, Che J. Personre-identification algorithm based on feature fusion and subspace learning[J]. Laser & Optoelectronics Progress, 2019, 56(2): 021503.

[3] 黄新宇, 许娇龙, 郭纲, 等. 基于增强聚合通道特征的实时行人重识别[J]. 激光与光电子学进展, 2017, 54(9): 091001.

    Huang X Y, Xu J L, Guo G, et al. Real-time pedestrian reidentification based on enhanced aggregated channel features[J]. Laser & Optoelectronics Progress, 2017, 54(9): 091001.

[4] Liu CX, Gong SG, Loy CC, et al. Person re-identification: What features are important?[M] ∥Fusiello A, Murino V, Cucchiara R. Computer Vision-ECCV 2012. Workshops and Demonstrations. Berlin, Heidelberg: Springer, 2012, 7583: 391- 401.

[5] LiW, Wang XG. Locally aligned feature transforms across views[C]∥2013 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2013, Portland, OR, USA. New York: IEEE, 2013: 3594- 3601.

[6] GrayD, TaoH. Viewpoint invariant pedestrian recognition with an ensemble of localized features[M] ∥Forsyth D, Torr P, Zisserman A. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2008, 5302: 262- 275.

[7] Ma BP, SuY, JurieF. Local descriptors encoded by fisher vectors for person re-identification[M] ∥Fusiello A, Murino V, Cucchiara R. Computer Vision-ECCV 2012. Workshops and Demonstrations. Berlin, Heidelberg: Springer, 2012, 7583: 413- 422.

[8] KöstingerM, HirzerM, WohlhartP, et al. Large scale metric learning from equivalence constraints[C]∥2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA. New York: IEEE, 2012: 2288- 2295.

[9] PedagadiS, OrwellJ, VelastinS, et al. Local fisher discriminant analysis for pedestrian re-identification[C]∥2013 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2013, Portland, OR, USA. New York: IEEE, 2013: 3318- 3325.

[10] XiongF, Gou MR, CampsO, et al. Person re-identification using kernel-based metric learning methods[M] ∥Fleet D, Pajdla T, Schiele B, et al. Computer Vision-ECCV 2014. Cham: Springer, 2014, 8695: 1- 16.

[11] Liao SC, HuY, Zhu XY, et al. Person re-identification by local maximal occurrence representation and metric learning[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 2197- 2206.

[12] Vaquero DA, Feris RS, TranD, et al. Attribute-based people search in surveillance environments[C]∥2009 Workshop on Applications of Computer Vision (WACV), December 7-8, 2009, Snowbird, UT, USA. New York: IEEE, 2009: 5403131.

[13] LayneR, Hospedales TM, GongS, et al. Attributes-based re-identification[M] ∥Gong S, Cristani M, Yan S, et al. Person re-identification. London: Springer, 2014: 93- 117.

[14] Zhu JQ, Liao SC, LeiZ, et al. Pedestrian attribute classification in surveillance: database and evaluation[C]∥2013 IEEE International Conference on Computer Vision Workshops, December 2-8, 2013, Sydney, NSW, Australia. New York: IEEE, 2013: 331- 338.

[15] Zhu JQ, Liao SC, YiD, et al. Multi-label CNN based pedestrian attribute learning for soft biometrics[C]∥2015 International Conference on Biometrics (ICB), May 19-22, 2015, Phuket, Thailand. New York: IEEE, 2015: 535- 540.

[16] Huang JS, FerisR, ChenQ, et al. Cross-domain image retrieval with a dual attribute-aware ranking network[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2015: 1062- 1070.

[17] JouB, Chang SF. Deep cross residual learning for multitask visual recognition[C]∥Proceedings of the 24th ACM International Conference on Multimedia, October 15-19, 2016, Amsterdam, The Netherlands. New York: ACM, 2016: 998- 1007.

[18] LeeG, YangE, Hwang SJ. Asymmetric multi-task learning based on task relatedness and loss[C]∥Proceedings of the 33rd International Conference on International Conference on Machine Learning, June 19-24, 2016, New York, USA. Massachusetts: JMLR. org, 2016, 48: 230- 238.

[19] Ranjan R, Patel V M, Chellappa R. HyperFace:a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(1): 121-135.

[20] YosinskiJ, CluneJ, BengioY, et al. How transferable are features in deep neural networks?[J/OL]. ( 2014-11-06)[2018-12-15]. https:∥arxiv.org/abs/1411. 1792.

[21] Lu YX, KumarA, Zhai SF, et al. Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 1131- 1140.

[22] He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 770- 778.

[23] ZhengL, YangY, Hauptmann A G. Person re-identification: past, present and future[J/OL]. ( 2016-10-10)[2018-12-15]. https:∥arxiv.org/abs/1610. 02984.

[24] Zhang ZP, LuoP, Loy CC, et al. Facial landmark detection by deep multi-task learning[M] ∥Fleet D, Pajdla T, Schiele B, et al. Computer Vision-ECCV 2014. Cham: Springer, 2014, 8694: 94- 108.

[25] Zhang Z P, Luo P, Loy C C, et al. Learning deep representation for face alignment with auxiliary attributes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(5): 918-930.

[26] LinY, ZhengL, ZhengZ, et al. Improving person re-identification by attribute and identitylearning[J/OL]. ( 2017-04-16)[2018-12-15]. https:∥arxiv.org/abs/1703. 07220.

[27] JiaY, ShelhamerE, DonahueJ, et al. Caffe: convolutional architecture for fast feature embedding[J/OL]. ( 2014-06-20)[2018-12-16]. https:∥arxiv.org/abs/1408. 5093.

[28] Abdulnabi A H, Wang G, Lu J W, et al. Multi-task CNN model for attribute prediction[J]. IEEE Transactions on Multimedia, 2015, 17(11): 1949-1959.

[29] Chen YQ, DuffnerS, StoianA, et al. Triplet CNN and pedestrian attribute recognition for improved person Re-identification[C]∥2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), August 29-September 1, 2017, Lecce, Italy. New York: IEEE, 2017: 8078542.

[30] Chen HR, Wang YW, Shi YM, et al. Deep transfer learning for person Re-identification[C]∥2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM), September 13-16, 2018, Xi'an, China. New York: IEEE, 2018: 8499067.

李净, 管业鹏. 基于深度学习行人属性自适应权重分配行人再识别方法[J]. 激光与光电子学进展, 2019, 56(14): 141003. Li Jing, Yepeng Guan. Pedestrian Re-Identification Based on Adaptive Weight Assignment using Deep Learning for Pedestrian Attributes[J]. Laser & Optoelectronics Progress, 2019, 56(14): 141003.

本文已被 1 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!