基于特征融合网络的行人重识别
|
摘要
行人重识别旨在大规模的分布式监控系统中进行行人目标匹配,紧凑且具有鲁棒性的特征表达对其至关重要,为此,本文提出了一种基于特征融合网络的特征提取方法.首先,利用STEL算法增强了LOMO特征对背景噪声的抗噪性能,利用KPCA算法降低维度以便于后续融合.随后,本文探索了手工特征和CNN特征的互补性,将改进LOMO特征融入至卷积神经网络之中,得到了区分度更高的融合特征.在VIPeR和CUHK01数据集上的测试结果表明,本文融合特征的区分度明显高于单一特征和级联特征, Rank-1较级联特征分别提高了3.73%和2.36%.
Person re-identification aims at pedestrian target matching under distributed monitoring systems.Compact and robust feature is critical to it.For this reason,this study proposes a feature extraction method based on feature fusion network.Firstly,the STEL algorithm is used to enhance the immunity of LOMO feature to background noise,and the KPCA algorithm is used to reduce dimension.Subsequently,we explore the complementarity between manual features and Convolutional Neural Network(CNN) features,and integrate the improved LOMO feature into the CNN to obtain a fusion feature with better performance.Experiments on two datasets(VIPeR and CUHK01) validate the effectiveness of our proposal,the Rank-1 of fusion feature is 3.73% and 2.36% higher than the cascade feature,respectively.
Person re-identification aims at pedestrian target matching under distributed monitoring systems.Compact and robust feature is critical to it.For this reason,this study proposes a feature extraction method based on feature fusion network.Firstly,the STEL algorithm is used to enhance the immunity of LOMO feature to background noise,and the KPCA algorithm is used to reduce dimension.Subsequently,we explore the complementarity between manual features and Convolutional Neural Network(CNN) features,and integrate the improved LOMO feature into the CNN to obtain a fusion feature with better performance.Experiments on two datasets(VIPeR and CUHK01) validate the effectiveness of our proposal,the Rank-1 of fusion feature is 3.73% and 2.36% higher than the cascade feature,respectively.
引文
1 Gheissari N, Sebastian TB, Hartley R. Person reidentification using spatiotemporal appearance. Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, NY, USA. 2006.1528-1535.
2 Zheng L,Yang Y,Hauptmann AG. Person re-identification:Past, present and future. arXiv preprint arXiv:1610.02984,2016.
3 Liao SC, Hu Y, Zhu XY, et al. Person re-identification by local maximal occurrence representation and metric learning.Proceedings of 2005 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA. 2015.2197-2206.
4 Gray D, Brennan S, Tao H. Evaluating appearance models for recognition, reacquisition, and tracking. Proceedings of the 10th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance(PETS). Rio de Janeiro. 2007, 3:1-7.
5 Farenzena M, Bazzani L,Perina A,et al. Person reidentification by symmetry-driven accumulation of local features. Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Francisco, CA, USA. 2010. 2360-2367.
6 Andriluka M,Roth S, Schiele B. Pictorial structures revisited:People detection and articulated pose Estimation.Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA. 2009.1014-1021.
7 Liao SC, Zhao GY, Kellokumpu V, et al. Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA. 2010.1301-1306.
8 Wu SX, Chen YC, Li X, et al. An enhanced deep feature representation for person re-identification. Proceedings of2016 IEEE Winter Conference on Applications of Computer Vision. Lake Placid, NY, USA. 2016. 1-8.
9 Scholkopf B, Smola A, Muller KR. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1998, 10(5):1299-1319.[doi:10.1162/089976698300017467]
10章登义,王骞,朱波,等.利用人体部位特征重要性进行行人再识别.武汉大学学报·信息科学版,2017, 42(1):84-90.
11 Jobson DJ,Rahman Z,Woodell GA. Properties and performance of a center/surround retinex. IEEE Transactions on Image Processing, 1997, 6(3):451-462.[doi:10.1109/83.557356]
12张田昊.数据降维算法研究及其应用[博士学位论文].上海:上海交通大学,2008.
13 Yang Y, Yang J M, Yan JJ, et al. Salient color names for person re-identification. European Conference on Computer Vision. Zurich. Switzerland. 2014. 536-551.
14 Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks.Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, NV, USA.2012. 1097-1105.
15 Gray D, Tao H. Viewpoint invariant pedestrian recognition with an ensemble of localized features. European Conference on Computer Vision. Marseille, France. 2008. 262-275.
16 Li W, Zhao R,Wang XG. Human reidentification with transferred metric learning. Asian Conference on Computer Vision. Daejeon, Korea. 2012. 31-44.
17 Ma BP, Su Y, Jurie F. Covariance descriptor based on bioinspired features for person re-identification and face verification. Image and Vision Computing, 2014, 32(6-7):379-390.
2 Zheng L,Yang Y,Hauptmann AG. Person re-identification:Past, present and future. arXiv preprint arXiv:1610.02984,2016.
3 Liao SC, Hu Y, Zhu XY, et al. Person re-identification by local maximal occurrence representation and metric learning.Proceedings of 2005 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA. 2015.2197-2206.
4 Gray D, Brennan S, Tao H. Evaluating appearance models for recognition, reacquisition, and tracking. Proceedings of the 10th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance(PETS). Rio de Janeiro. 2007, 3:1-7.
5 Farenzena M, Bazzani L,Perina A,et al. Person reidentification by symmetry-driven accumulation of local features. Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Francisco, CA, USA. 2010. 2360-2367.
6 Andriluka M,Roth S, Schiele B. Pictorial structures revisited:People detection and articulated pose Estimation.Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA. 2009.1014-1021.
7 Liao SC, Zhao GY, Kellokumpu V, et al. Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA. 2010.1301-1306.
8 Wu SX, Chen YC, Li X, et al. An enhanced deep feature representation for person re-identification. Proceedings of2016 IEEE Winter Conference on Applications of Computer Vision. Lake Placid, NY, USA. 2016. 1-8.
9 Scholkopf B, Smola A, Muller KR. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1998, 10(5):1299-1319.[doi:10.1162/089976698300017467]
10章登义,王骞,朱波,等.利用人体部位特征重要性进行行人再识别.武汉大学学报·信息科学版,2017, 42(1):84-90.
11 Jobson DJ,Rahman Z,Woodell GA. Properties and performance of a center/surround retinex. IEEE Transactions on Image Processing, 1997, 6(3):451-462.[doi:10.1109/83.557356]
12张田昊.数据降维算法研究及其应用[博士学位论文].上海:上海交通大学,2008.
13 Yang Y, Yang J M, Yan JJ, et al. Salient color names for person re-identification. European Conference on Computer Vision. Zurich. Switzerland. 2014. 536-551.
14 Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks.Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, NV, USA.2012. 1097-1105.
15 Gray D, Tao H. Viewpoint invariant pedestrian recognition with an ensemble of localized features. European Conference on Computer Vision. Marseille, France. 2008. 262-275.
16 Li W, Zhao R,Wang XG. Human reidentification with transferred metric learning. Asian Conference on Computer Vision. Daejeon, Korea. 2012. 31-44.
17 Ma BP, Su Y, Jurie F. Covariance descriptor based on bioinspired features for person re-identification and face verification. Image and Vision Computing, 2014, 32(6-7):379-390.