Abstract
Video-based person re-identification aims to precisely match video sequences of pedestrian across non-overlapped cameras. Existing methods deal with this task by encoding each frame and aggregating them along time. In order to increase the discriminative ability of video features, we propose an end-to-end framework called Multi-grained Fusion Network (MGFN) which aims to keep both global and local information by combining frame-level representations with different granularities. The final video features are generated by aggregating multi-grained representations on both spatial and temporal. Experiments indicate our method achieves excellent performance on three widely used datasets named PRID-2011, iLIDS-VID, and MARS. Especially on MARS, MGFN surpass state-of-the-art result by \(11.5\%\).
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References
Chen, J., Wang, Y., Tang, Y.Y.: Person re-identification by exploiting spatio-temporal cues and multi-view metric learning. IEEE Sig. Process. Lett. 23(7), 998–1002 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hirzer, M., Beleznai, C., Roth, P.M., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Scandinavian Conference on Image Analysis, pp. 91–102 (2011)
Liu, H., Jie, Z., Jayashree, K., Qi, M., Jiang, J., Yan, S., Feng, J.: Video-based person re-identification with accumulative motion context. IEEE Trans. Circ. Syst. Video Technol. PP(99), 1–1 (2017)
Liu, Y., Yan, J., Ouyang, W.: Quality aware network for set to set recognition. In: Computer Vision and Pattern Recognition, pp. 5790–5799 (2017)
Mclaughlin, N., Rincon, J.M.D., Miller, P.: Recurrent convolutional network for video-based person re-identification. In: Computer Vision and Pattern Recognition, pp. 1325–1334. IEEE (2016)
Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: European Conference on Computer Vision Workshop on Benchmarking Multi-target Tracking, pp. 17–35 (2016)
Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: person retrieval with refined part pooling. arXiv preprint arXiv:1711.09349 (2017)
Wang, T., Gong, S., Zhu, X., Wang, S.: Person re-identification by video ranking. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 688–703. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_45
Wei, L., Zhang, S., Yao, H., Gao, W., Tian, Q.: Glad: Global-local-alignment descriptor for pedestrian retrieval. In: Proceedings of the 2017 ACM on Multimedia Conference, pp. 420–428. ACM (2017)
Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., Tian, Q.: MARS: a video benchmark for large-scale person re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 868–884. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_52
Zhou, Z., Huang, Y., Wang, W., Wang, L., Tan, T.: See the forest for the trees: joint spatial and temporal recurrent neural networks for video-based person re-identification. In: Computer Vision and Pattern Recognition, pp. 6776–6785 (2017)
Acknowledgement
This study is partially supported by the National Key R&D Program of China (No. 2017YFB1002000 ), the National Natural Science Foundation of China (No. 61472019), the Macao Science and Technology Development Fund (No. 138/2016/A3), the Program of Introducing Talents of Discipline to Universities and the Open Fund of the State Key Laboratory of Software Development Environment under grant SKLSDE-2017ZX-09, the Project of Experimental Verification of the Basic Commonness and Key Technical Standards of the Industrial Internet network architecture. Thank you for the support from HAWKEYE Group.
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Li, C., Liu, L., Lv, K., Sheng, H., Ke, W. (2018). Combine Coarse and Fine Cues: Multi-grained Fusion Network for Video-Based Person Re-identification. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_16
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