Abstract
Person re-identification is the problem of matching pedestrian images captured from multiple cameras. Feature representation and metric designing are two critical aspects in person re-identification. In this paper, we first propose an effective Convolutional Neural Network and learn it with mixed datasets as a general deep feature extractor. Secondly, we extract the hand-crafted feature of images as a supplement, then we learn the independent distance metrics for deep feature representation and hand-crafted feature representation, respectively. Finally, we validate our method on three challenging person re-identification datasets, experimental results show the effectiveness of our approach, and we achieve the best rank-1 matching rates on all the three datasets compare with the state-of-the-art methods.
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References
Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 262–275. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88682-2_21
Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: Computer Vision and Pattern Recognition (CVPR), vol. 23, pp. 2360–2367 (2010)
Zhao, R., Ouyang, W., Wang, X.: Learning mid-level filters for person re-identification. In: CVPR, pp. 144–151 (2014)
Hu, Y., Liao, S., Lei, Z., Yi, D., Li, S.Z.: Exploring structural information and fusing multiple features for person re-identification. In: Computer Vision and Pattern Recognition Workshops (CVPRW), vol. 13, pp. 794–799 (2013)
Zhao, R., Ouyang, W., Wang, X.: Person re-identification by salience matching. In: Computer Vision (ICCV), pp. 2528–2535 (2013)
Ma, B., Su, Y., Jurie, F.: Local descriptors encoded by fisher vectors for person re-identification. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012. LNCS, vol. 7583, pp. 413–422. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33863-2_41
Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: CVPR, vol. 8, pp. 2197–2206 (2015)
Ma, B., Su, Y., Jurie, F.: Covariance descriptor based on bio-inspired features for person re-identification and face verification. Image Vis. Comput. 32, 379–390 (2014)
Koestinger, M., Hirzer, M., Wohlhart, P.: Large scale metric learning from equivalence constraints. In: CVPR, pp. 2288–2295 (2012)
McFee, B., Lanckriet, G.R.G.: Metric learning to rank. In: International Conference on Machine Learning, pp. 775–782 (2010)
Mignon, A., Jurie, F.: PCCA: a new approach for distance learning from sparse pairwise constraints. In: CVPR, vol. 157, pp. 2666–2672 (2012)
Hirzer, M., Roth, P.M., Köstinger, M., Bischof, H.: Relaxed pairwise learned metric for person re-identification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 780–793. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33783-3_56
Zheng, W.S., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: CVPR, vol. 42, pp. 649–656 (2011)
Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207–244 (2009)
Li, W., Zhao, R., Xiao, T., Wang, X.: Deepreid: deep filter pairing neural network for person re-identification. In: CVPR, pp. 152–159 (2014)
Yi, D., Lei, Z., Li, S.Z.: Deep metric learning for practical person re-identification. In: ICPR, pp. 34–39 (2014)
Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. In: CVPR, pp. 3908–3916 (2015)
Wu, L., Shen, C., Hengel, A.V.D.: Personnet: person reidentification with deep convolutional neural networks. arXiv preprint arXiv:1601.07255 (2016)
Xiao, T., Li, H., Ouyang, W., Wang, X.: Learning deep feature representations with domain guided dropout for person re-identification. arXiv preprint arXiv:1604.07528 (2016)
Szegedy, C., Vanhoucke, V., IoffeSzegedy, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. arXiv preprint arXiv:1512.0056 (2015)
Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS), vol. 3 (2007)
Li, W., Zhao, R., Wang, X.: Human reidentification with transferred metric learning. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 31–44. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37331-2_3
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010)
Paisitkriangkrai, S., Shen, C., Hengel, A.V.D.: Learning to rank in person re-identification with metric ensembles. arXiv preprint arXiv:1503.01543 (2015)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: ACM, pp. 675–678 (2014)
Moon, H., Phillips, P.J.: Evaluating appearance models for recognition, reacquisition, and tracking. Perception 30, 303–321 (2001)
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Qi, M., Han, J., Jiang, J. (2016). Person Re-identification by Multiple Feature Representations and Metric Learning. In: Zhang, Z., Huang, K. (eds) Intelligent Visual Surveillance. IVS 2016. Communications in Computer and Information Science, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-10-3476-3_10
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DOI: https://doi.org/10.1007/978-981-10-3476-3_10
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