Matching People Across Surveillance Cameras
Resumo
This work addresses the person re-identification problem, which consists on matching images of individuals captured by multiple and non-overlapping surveillance cameras. Works from literature tackle this problem proposing robust feature descriptors and matching functions, where the latter is responsible to assign the correct identity for individuals and is the focus of this work. Specifically, we propose two matching methods: the Kernel MBPLS and the Kernel X-CRC. The Kernel MBPLS is a nonlinear regression model that is scalable with respect to the number of cameras and allows the inclusion of additional labelled information (e.g., attributes). Differently, the Kernel X-CRC is a nonlinear and multitask matching function that can be used jointly with subspace learning approaches to boost the matching rates. We present an extensive experimental evaluation of both approaches in four datasets (VIPeR, PRID450S, WARD and Market-1501). Experimental results demonstrate that the Kernel MBPLS and the Kernel X-CRC outperforms approaches from literature. Furthermore, we show that the Kernel X-CRC can be successfuly applied in large-scale and multiple cameras datasets.
Referências
R. Prates and W. R. Schwartz, “Matching people across surveillance cameras,” Ph.D. dissertation, Department of Computer Science, UFMG, Belo Horizonte, Brazil, April 2019.
L. Zheng, Y. Yang, and A. G. Hauptmann, “Person re-identification: Past, present and future,” arXiv preprint arXiv:1610.02984, 2016.
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.
S. Liao, Y. Hu, X. Zhu, and S. Z. Li, “Person re-identification by local maximal occurrence representation and metric learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2197–2206. https://doi.org/10.1109/CVPR.2015.7298832
T. Matsukawa, T. Okabe, E. Suzuki, and Y. Sato, “Hierarchical gaussian descriptor for person re-identification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1363–1372. https://doi.org/10.1109/CVPR.2016.152
G. Lisanti, I. Masi, A. Bagdanov, and A. Del Bimbo, “Person re-identification by iterative re-weighted sparse ranking,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 37, no. 8, pp. 1629–1642, Aug 2015. https://doi.org/10.1109/TPAMI.2014.2369055
G. Lisanti, I. Masi, and A. Del Bimbo, “Matching people across camera views using kernel canonical correlation analysis,” in Proceedings of the International Conference on Distributed Smart Cameras, ser. ICDSC ’14. New York, NY, USA: ACM, 2014, pp. 10:1–10:6. https://doi.org/10.1145/2659021.2659036
R. Prates, M. Oliveira, and W. R. Schwartz, “Kernel partial least squares for person re-identification,” in IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2016. https://doi.org/10.1109/AVSS.2016.7738030
Z. Wei-Shi, G. Shaogang, and X. Tao, “Person re-identification by probabilistic relative distance comparison,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011, pp. 203–208. https://doi.org/10.1109/CVPR.2011.5995598
R. R. Varior, B. Shuai, J. Lu, D. Xu, and G. Wang, “A siamese long short-term memory architecture for human re-identification,” in European Conference on Computer Vision. Springer, 2016, pp. 135–153. https://doi.org/10.1007/978-3-319-46478-7_9
F. Xiong, M. Gou, O. Camps, and M. Sznaier, “Person re-identification using kernel-based metric learning methods,” in European Conference on Computer Vision (ECCV). Springer, 2014, pp. 1–16. https://doi.org/10.1007/978-3-319-10584-0_1
L. Zhang, T. Xiang, and S. Gong, “Learning a discriminative null space for person re-identification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1239–1248. https://doi.org/10.1109/CVPR.2016.139
C. Jose and F. Fleuret, “Scalable metric learning via weighted approximate rank component analysis,” arXiv preprint arXiv:1603.00370, 2016.
M. Zeng, Z. Wu, C. Tian, L. Zhang, and L. Hu, “Efficient person re-identification by hybrid spatiogram and covariance descriptor,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015, pp. 48–56. https://doi.org/10.1109/CVPRW.2015.7301296
S. Karanam, Y. Li, and R. Radke, “Sparse re-id: Block sparsity for person re-identification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015, pp. 33–40. https://doi.org/10.1109/CVPRW.2015.7301392
M. T. Harandi, C. Sanderson, R. Hartley, and B. C. Lovell, “Sparse coding and dictionary learning for symmetric positive definite matrices: A kernel approach,” in Computer Vision–ECCV 2012. Springer, 2012, pp. 216–229. https://doi.org/10.1007/978-3-642-33709-3_16
E. Kodirov, T. Xiang, and S. Gong, “Dictionary learning with iterative laplacian regularisation for unsupervised person re-identification,” in Proceedings of the British Machine Vision Conference (BMVC). BMVA Press, September 2015, pp. 44.1–44.12. https://dx.doi.org/10.5244/C.29.44
R. Prates and W. R. Schwartz, “Kernel multiblock partial least squares for a scalable and multicamera person reidentification system,” Journal of Electronic Imaging, vol. 27, no. 3, p. 033041, 2018. https://doi.org/10.1117/1.JEI.27.3.033041
R. Prates and W. R. Schwartz, “Kernel cross-view collaborative representation based classification for person re-identification,” Journal of Visual Communication and Image Representation, 2018. https://doi.org/10.1016/j.jvcir.2018.12.003
R. Prates, M. Oliveira, and W. R. Schwartz, “Kernel partial least squares for person re-identification,” in IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2016. https://doi.org/10.1109/AVSS.2016.7738030
R. Prates and W. R. Schwartz, “Kernel hierarchical pca for person re-identification,” in 23th International Conference on Pattern Recognition, ICPR 2016, Cancun, MEXICO, December 4-8, 2016., 2016. https://doi.org/10.1109/ICPR.2016.7899944
R. Prates and W. R. Schwartz, “Appearance-based person re-identification by intra-camera dis-criminative models and rank aggregation,” in International Conference on Biometrics, ICB 2015, Phuket, Thailand, 19-22 May, 2015, 2015, pp. 65–72. https://doi.org/10.1109/ICB.2015.7139077
C. D. Raphael Prates and W. R. Schwartz, “Predominant color name indexing structure for person re-identification,” in 2016 IEEE International Conference on Image Processing (ICIP). Springer, 2016, pp. 779–783. https://doi.org/10.1109/ICIP.2016.7532463
R. Prates and W. R. Schwartz, “Cbra: Color-based ranking aggregation for person re-identification,” in Image Processing (ICIP), 2015 IEEE International Conference on. IEEE, 2015, pp. 1975–1979. https://doi.org/10.1109/ICIP.2015.7351146
H. Wold, Encyclopedia of Statistical Sciences. John Wiley & Sons, 1985, vol. 6. https://www.doi.org/10.1002/0471667196
D. Gray and H. Tao, “Viewpoint invariant pedestrian recognition with an ensemble of localized features,” in European conference on computer vision. Springer, 2008, pp. 262–275. https://doi.org/10.1007/978-3-540-88682-2_21
P. M. Roth, M. Hirzer, M. Köstinger, C. Beleznai, and H. Bischof, “Mahalanobis distance learning for person re-identification,” in Person Re-Identification. Springer, 2014, pp. 247–267. https://doi.org/10.1007/978-1-4471-6296-4_12
L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian, “Scalable person re-identification: A benchmark,” in Computer Vision, IEEE International Conference on, 2015. https://doi.org/10.1109/ICCV.2015.133
N. Martinel and C. Micheloni, “Re-identify people in wide area camera network,” in Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on. IEEE, 2012, pp. 31–36. https://doi.org/10.1109/CVPRW.2012.6239203
W. Shangxuan, C. Ying-Cong, L. Xiang, Y. Jin-Jie, and Z. Wei-Shi, “An enhanced deep feature representation for person re-identification,” in WACV2016: IEEE Winter Conference on Applications of Computer Vision., March 2016. https://doi.org/10.1109/WACV.2016.7477681
S. Liao and S. Z. Li, “Efficient psd constrained asymmetric metric learning for person re-identification,” in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 3685–3693. https://doi.org/10.1109/ICCV.2015.420
J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proceedings of the IEEE, vol. 98, no. 6, pp. 1031–1044, 2010. https://doi.org/10.1109/JPROC.2010.2044470
L. Zhang, M. Yang, X. Feng, Y. Ma, and D. Zhang, “Collaborative representation based classification for face recognition,” arXiv preprint arXiv:1204.2358, 2012.
S.-Z. Chen, C.-C. Guo, and J.-H. Lai, “Deep ranking for person re-identification via joint representation learning,” IEEE Transactions on Image Processing, vol. 25, no. 5, pp. 2353–2367, 2016. https://doi.org/10.1109/TIP.2016.2545929
D. Cheng, Y. Gong, S. Zhou, J. Wang, and N. Zheng, “Person re-identification by multi-channel parts-based cnn with improved triplet loss function,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1335–1344. https://doi.org/10.1109/CVPR.2016.149
D. Chen, Z. Yuan, B. Chen, and N. Zheng, “Similarity learning with spatial constraints for person re-identification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1268–1277. https://doi.org/10.1109/CVPR.2016.142
L. Zheng, Y. Yang, and A. G. Hauptmann, “Person re-identification: Past, present and future,” arXiv preprint arXiv:1610.02984, 2016.
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.
S. Liao, Y. Hu, X. Zhu, and S. Z. Li, “Person re-identification by local maximal occurrence representation and metric learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2197–2206. https://doi.org/10.1109/CVPR.2015.7298832
T. Matsukawa, T. Okabe, E. Suzuki, and Y. Sato, “Hierarchical gaussian descriptor for person re-identification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1363–1372. https://doi.org/10.1109/CVPR.2016.152
G. Lisanti, I. Masi, A. Bagdanov, and A. Del Bimbo, “Person re-identification by iterative re-weighted sparse ranking,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 37, no. 8, pp. 1629–1642, Aug 2015. https://doi.org/10.1109/TPAMI.2014.2369055
G. Lisanti, I. Masi, and A. Del Bimbo, “Matching people across camera views using kernel canonical correlation analysis,” in Proceedings of the International Conference on Distributed Smart Cameras, ser. ICDSC ’14. New York, NY, USA: ACM, 2014, pp. 10:1–10:6. https://doi.org/10.1145/2659021.2659036
R. Prates, M. Oliveira, and W. R. Schwartz, “Kernel partial least squares for person re-identification,” in IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2016. https://doi.org/10.1109/AVSS.2016.7738030
Z. Wei-Shi, G. Shaogang, and X. Tao, “Person re-identification by probabilistic relative distance comparison,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011, pp. 203–208. https://doi.org/10.1109/CVPR.2011.5995598
R. R. Varior, B. Shuai, J. Lu, D. Xu, and G. Wang, “A siamese long short-term memory architecture for human re-identification,” in European Conference on Computer Vision. Springer, 2016, pp. 135–153. https://doi.org/10.1007/978-3-319-46478-7_9
F. Xiong, M. Gou, O. Camps, and M. Sznaier, “Person re-identification using kernel-based metric learning methods,” in European Conference on Computer Vision (ECCV). Springer, 2014, pp. 1–16. https://doi.org/10.1007/978-3-319-10584-0_1
L. Zhang, T. Xiang, and S. Gong, “Learning a discriminative null space for person re-identification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1239–1248. https://doi.org/10.1109/CVPR.2016.139
C. Jose and F. Fleuret, “Scalable metric learning via weighted approximate rank component analysis,” arXiv preprint arXiv:1603.00370, 2016.
M. Zeng, Z. Wu, C. Tian, L. Zhang, and L. Hu, “Efficient person re-identification by hybrid spatiogram and covariance descriptor,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015, pp. 48–56. https://doi.org/10.1109/CVPRW.2015.7301296
S. Karanam, Y. Li, and R. Radke, “Sparse re-id: Block sparsity for person re-identification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015, pp. 33–40. https://doi.org/10.1109/CVPRW.2015.7301392
M. T. Harandi, C. Sanderson, R. Hartley, and B. C. Lovell, “Sparse coding and dictionary learning for symmetric positive definite matrices: A kernel approach,” in Computer Vision–ECCV 2012. Springer, 2012, pp. 216–229. https://doi.org/10.1007/978-3-642-33709-3_16
E. Kodirov, T. Xiang, and S. Gong, “Dictionary learning with iterative laplacian regularisation for unsupervised person re-identification,” in Proceedings of the British Machine Vision Conference (BMVC). BMVA Press, September 2015, pp. 44.1–44.12. https://dx.doi.org/10.5244/C.29.44
R. Prates and W. R. Schwartz, “Kernel multiblock partial least squares for a scalable and multicamera person reidentification system,” Journal of Electronic Imaging, vol. 27, no. 3, p. 033041, 2018. https://doi.org/10.1117/1.JEI.27.3.033041
R. Prates and W. R. Schwartz, “Kernel cross-view collaborative representation based classification for person re-identification,” Journal of Visual Communication and Image Representation, 2018. https://doi.org/10.1016/j.jvcir.2018.12.003
R. Prates, M. Oliveira, and W. R. Schwartz, “Kernel partial least squares for person re-identification,” in IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2016. https://doi.org/10.1109/AVSS.2016.7738030
R. Prates and W. R. Schwartz, “Kernel hierarchical pca for person re-identification,” in 23th International Conference on Pattern Recognition, ICPR 2016, Cancun, MEXICO, December 4-8, 2016., 2016. https://doi.org/10.1109/ICPR.2016.7899944
R. Prates and W. R. Schwartz, “Appearance-based person re-identification by intra-camera dis-criminative models and rank aggregation,” in International Conference on Biometrics, ICB 2015, Phuket, Thailand, 19-22 May, 2015, 2015, pp. 65–72. https://doi.org/10.1109/ICB.2015.7139077
C. D. Raphael Prates and W. R. Schwartz, “Predominant color name indexing structure for person re-identification,” in 2016 IEEE International Conference on Image Processing (ICIP). Springer, 2016, pp. 779–783. https://doi.org/10.1109/ICIP.2016.7532463
R. Prates and W. R. Schwartz, “Cbra: Color-based ranking aggregation for person re-identification,” in Image Processing (ICIP), 2015 IEEE International Conference on. IEEE, 2015, pp. 1975–1979. https://doi.org/10.1109/ICIP.2015.7351146
H. Wold, Encyclopedia of Statistical Sciences. John Wiley & Sons, 1985, vol. 6. https://www.doi.org/10.1002/0471667196
D. Gray and H. Tao, “Viewpoint invariant pedestrian recognition with an ensemble of localized features,” in European conference on computer vision. Springer, 2008, pp. 262–275. https://doi.org/10.1007/978-3-540-88682-2_21
P. M. Roth, M. Hirzer, M. Köstinger, C. Beleznai, and H. Bischof, “Mahalanobis distance learning for person re-identification,” in Person Re-Identification. Springer, 2014, pp. 247–267. https://doi.org/10.1007/978-1-4471-6296-4_12
L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian, “Scalable person re-identification: A benchmark,” in Computer Vision, IEEE International Conference on, 2015. https://doi.org/10.1109/ICCV.2015.133
N. Martinel and C. Micheloni, “Re-identify people in wide area camera network,” in Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on. IEEE, 2012, pp. 31–36. https://doi.org/10.1109/CVPRW.2012.6239203
W. Shangxuan, C. Ying-Cong, L. Xiang, Y. Jin-Jie, and Z. Wei-Shi, “An enhanced deep feature representation for person re-identification,” in WACV2016: IEEE Winter Conference on Applications of Computer Vision., March 2016. https://doi.org/10.1109/WACV.2016.7477681
S. Liao and S. Z. Li, “Efficient psd constrained asymmetric metric learning for person re-identification,” in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 3685–3693. https://doi.org/10.1109/ICCV.2015.420
J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proceedings of the IEEE, vol. 98, no. 6, pp. 1031–1044, 2010. https://doi.org/10.1109/JPROC.2010.2044470
L. Zhang, M. Yang, X. Feng, Y. Ma, and D. Zhang, “Collaborative representation based classification for face recognition,” arXiv preprint arXiv:1204.2358, 2012.
S.-Z. Chen, C.-C. Guo, and J.-H. Lai, “Deep ranking for person re-identification via joint representation learning,” IEEE Transactions on Image Processing, vol. 25, no. 5, pp. 2353–2367, 2016. https://doi.org/10.1109/TIP.2016.2545929
D. Cheng, Y. Gong, S. Zhou, J. Wang, and N. Zheng, “Person re-identification by multi-channel parts-based cnn with improved triplet loss function,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1335–1344. https://doi.org/10.1109/CVPR.2016.149
D. Chen, Z. Yuan, B. Chen, and N. Zheng, “Similarity learning with spatial constraints for person re-identification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1268–1277. https://doi.org/10.1109/CVPR.2016.142
Publicado
28/10/2019
Como Citar
PRATES, Raphael; SCHWARTZ, William Robson.
Matching People Across Surveillance Cameras. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 84-90.
DOI: https://doi.org/10.5753/sibgrapi.est.2019.8306.
