Abstract
Person re-identification (Re-ID) is a key technology for intelligent video analysis. However, it is still a challenging task due to various complex background, different poses of person, etc. In this paper we try to address this issue by proposing a novel method based on person segmentation. Contrary to the previous method, we segment the person region from the image first. A pose-aware segmentation method (PA) is proposed by introducing the human pose into segmentation scheme. Then the deep learning features are extracted based on the person region instead of the whole bounding box. Finally, the person Re-ID results are acquired through the rank of Euclidean distance. Comprehensive experiments on two public person Re-ID datasets show the effectiveness of our method and the comparison experiments demonstrate that our method can outperform the state-of-the-art method.
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References
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010)
Zheng, W.S., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: Computer Vision and Pattern Recognition, pp. 649–656 (2011)
Li, A., Liu, L., Yan, S.: Person re-identification by attribute-assisted clothes appearance. In: Gong, S., Cristani, M., Yan, S., Loy, C.C. (eds.) Person Re-Identification. ACVPR, pp. 119–138. Springer, London (2014). https://doi.org/10.1007/978-1-4471-6296-4_6
Zheng, L., Huang, Y., Lu, H., Yang, Y.: Pose invariant embedding for deep person re-identification (2017)
Zhao, R., Ouyang, W., Wang, X.: Person re-identification by saliency learning. IEEE Trans. Pattern Anal. Mach. Intell. 39, 356–370 (2016)
Chen, Y.C., Zhu, X., Zheng, W.S., Lai, J.H.: Person re-identification by camera correlation aware feature augmentation. IEEE Trans. Pattern Anal. Mach. Intell. 40, 392–408 (2018)
Roth, P.M., Hirzer, M., Köstinger, M., Beleznai, C., Bischof, H.: Mahalanobis distance learning for person re-identification. In: Gong, S., Cristani, M., Yan, S., Loy, C.C. (eds.) Person Re-Identification. ACVPR, pp. 247–267. Springer, London (2014). https://doi.org/10.1007/978-1-4471-6296-4_12
Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2197–2206 (2015)
Chen, Y., Zhao, C., Wang, X., Gao, C.: Robust color invariant model for person re-identification. In: You, Z., et al. (eds.) CCBR 2016. LNCS, vol. 9967, pp. 695–702. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46654-5_76
Wang, D., Yan, C., Shan, S., Chen, X.: Unsupervised person re-identification with locality-constrained Earth Mover’s distance. In: IEEE International Conference on Image Processing, pp. 4289–4293 (2016)
Wang, Z., Hu, R., Liang, C., Leng, Q., Sun, K.: Region-based interactive ranking optimization for person re-identification. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, C.-K., Huet, B., Ngo, C.-W. (eds.) PCM 2014. LNCS, vol. 8879, pp. 1–10. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13168-9_1
Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3652–3661 (2017)
Yuan, M., et al.: A framework with updateable joint images re-ranking for Person Re-identification (2018)
Yu, R., Zhou, Z., Bai, S., Bai, X.: Divide and fuse: a re-ranking approach for person re-identification (2017)
Liu, X., Liu, W., Mei, T., Ma, H.: A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 869–884. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_53
Liu, W., Mei, T., Zhang, Y., Che, C., Luo, J.: Multi-task deep visual-semantic embedding for video thumbnail selection. In: Computer Vision and Pattern Recognition, pp. 3707–3715 (2015)
Li, W., Zhao, R., Xiao, T., Wang, X.: DeepReID: deep filter pairing neural network for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 152–159 (2014)
Wang, F., Zuo, W., Lin, L., Zhang, D., Zhang, L.: Joint learning of single-image and cross-image representations for person re-identification. In: Computer Vision and Pattern Recognition, pp. 1288–1296 (2016)
Cheng, D., Gong, Y., Zhou, S., Wang, J., Zheng, N.: Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1335–1344 (2016)
Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: Joint detection and identification feature learning for person search (2016)
Zhao, H., et al.: Spindle net: person re-identification with human body region guided feature decomposition and fusion. In: Computer Vision and Pattern Recognition, pp. 907–915 (2017)
Lin, Y., Zheng, L., Zheng, Z., Wu, Y., Yang, Y.: Improving person re-identification by attribute and identity learning (2017)
Chen, W., Chen, X., Zhang, J., Huang, K.: Beyond triplet loss: a deep quadruplet network for person re-identification, pp. 1320–1329 (2017)
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). https://doi.org/10.1007/978-3-642-37331-2_3
Fan, H., Zheng, L., Yang, Y.: Unsupervised person re-identification: clustering and fine-tuning (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014)
Zheng, Z., Zheng, L., Yang, Y.: Pedestrian alignment network for large-scale person re-identification (2017)
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: IEEE International Conference on Computer Vision, pp. 1116–1124 (2016)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (No. 61672475, No. 61402428, 61702471); Qingdao Science and Technology Development Plan (No. 16-5-1-13-jch).
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Zhang, W., Wei, Z., Huang, L., Nie, J., Lv, L., Wei, G. (2019). Person Re-Identification Based on Pose-Aware Segmentation. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11296. Springer, Cham. https://doi.org/10.1007/978-3-030-05716-9_25
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