Abstract
Person search aims to apply pedestrian detection and person re-identification simultaneously to search persons in images, which inevitably introduces pedestrian box misalignment during the procedure. And the detected boxes usually have a large variety of scales on a single image. Together with cluttered background and occlusion, all these distracting factors make it difficult to extract discriminative pedestrian representations. However, these problems are usually ignored by current person search systems. In this work, we propose a novel Multilevel Collaborative Attention Network (MCAN) to fulfill person search task efficiently. A multilevel selective learning is introduced to extract scale-aware features in different levels, and a collaborative attention module consisting of hard regional attention and soft pixel-wise attention is designed to deal with misalignment, background noise and occlusion. MCAN achieves 60.1% top-1 accuracy and 29.1% mAP on PRW benchmark, demonstrating its superiority over current state-of-the-art methods.
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References
Cai, Z., Saberian, M., Vasconcelos, N.: Learning complexity-aware cascades for deep pedestrian detection. In: ICCV, pp. 3361–3369 (2015)
Ding, S., Lin, L., Wang, G., Chao, H.: Deep feature learning with relative distance comparison for person re-identification. Pattern Recognit. 48(10), 2993–3003 (2015)
Dollár, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. PAMI 36(8), 1532–1545 (2014)
Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: CVPR, pp. 2360–2367. IEEE (2010)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. PAMI 32(9), 1627–1645 (2010)
Gheissari, N., Sebastian, T.B., Hartley, R.: Person reidentification using spatiotemporal appearance. In: CVPR, vol. 2, pp. 1528–1535. IEEE (2006)
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). https://doi.org/10.1007/978-3-540-88682-2_21
Hamdoun, O., Moutarde, F., Stanciulescu, B., Steux, B.: Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences. In: ICDSC, pp. 1–6. IEEE (2008)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: ICCV, pp. 2980–2988. IEEE (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. arXiv preprint arXiv:1709.01507 (2017)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017–2025 (2015)
Koestinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: CVPR, pp. 2288–2295. IEEE (2012)
Li, S., Xiao, T., Li, H., Zhou, B., Yue, D., Wang, X.: Person search with natural language description
Li, W., Zhao, R., Xiao, T., Wang, X.: DeepReID: deep filter pairing neural network for person re-identification. In: CVPR, pp. 152–159 (2014)
Li, W., Zhu, X., Gong, S.: Harmonious attention network for person re-identification. arXiv preprint arXiv:1802.08122 (2018)
Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: CVPR, pp. 2197–2206 (2015)
Liao, S., Li, S.Z.: Efficient PSD constrained asymmetric metric learning for person re-identification. In: ICCV, pp. 3685–3693 (2015)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR, vol. 1, p. 4 (2017)
Liu, H., et al.: Neural person search machines. In: ICCV (2017)
Ma, L., Sun, Q., Georgoulis, S., Van Gool, L., Schiele, B., Fritz, M.: Disentangled person image generation. arXiv preprint arXiv:1712.02621 (2017)
Nam, W., Dollár, P., Han, J.H.: Local decorrelation for improved pedestrian detection. In: Advances in Neural Information Processing Systems, pp. 424–432 (2014)
Paisitkriangkrai, S., Shen, C., van den Hengel, A.: Learning to rank in person re-identification with metric ensembles. In: CVPR, pp. 1846–1855 (2015)
Pumarola, A., Agudo, A., Sanfeliu, A., Moreno-Noguer, F.: Unsupervised person image synthesis in arbitrary poses
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 761–769 (2016)
Song, G., Leng, B., Liu, Y., Hetang, C., Cai, S.: Region-based quality estimation network for large-scale person re-identification. arXiv preprint arXiv:1711.08766 (2017)
Su, C., Li, J., Zhang, S., Xing, J., Gao, W., Tian, Q.: Pose-driven deep convolutional model for person re-identification. In: ICCV, pp. 3980–3989. IEEE (2017)
Tao, D., Guo, Y., Song, M., Li, Y., Yu, Z., Tang, Y.Y.: Person re-identification by dual-regularized kiss metric learning. IEEE Trans. Image Process. 25(6), 2726–2738 (2016)
Tian, Y., Luo, P., Wang, X., Tang, X.: Deep learning strong parts for pedestrian detection. In: ICCV, pp. 1904–1912 (2015)
Wang, F., et al.: Residual attention network for image classification. arXiv preprint arXiv:1704.06904 (2017)
Wang, X., Doretto, G., Sebastian, T., Rittscher, J., Tu, P.: Shape and appearance context modeling. In: ICCV, pp. 1–8. IEEE (2007)
Wei, L., Zhang, S., Yao, H., Gao, W., Tian, Q.: GLAD: global-local-alignment descriptor for pedestrian retrieval. In: ACMMM, pp. 420–428. ACM (2017)
Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: End-to-end deep learning for person search. arXiv preprint (2016)
Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: Joint detection and identification feature learning for person search. In: CVPR, pp. 3376–3385. IEEE (2017)
Xu, Y., Ma, B., Huang, R., Lin, L.: Person search in a scene by jointly modeling people commonness and person uniqueness. In: ACMMM, pp. 937–940. ACM (2014)
Yang, B., Yan, J., Lei, Z., Li, S.Z.: Convolutional channel features. In: ICCV, pp. 82–90. IEEE (2015)
Zajdel, W., Zivkovic, Z., Krose, B.: Keeping track of humans: have I seen this person before? In: ICRA, pp. 2081–2086. IEEE (2005)
Zhang, L., Lin, L., Liang, X., He, K.: Is faster R-CNN doing well for pedestrian detection? In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 443–457. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_28
Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: CVPR, pp. 3586–3593. IEEE (2013)
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: ICCV, pp. 1116–1124 (2015)
Zheng, L., Zhang, H., Sun, S., Chandraker, M., Tian, Q.: Person re-identification in the wild. arXiv preprint (2017)
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: CVPR, pp. 6776–6785. IEEE (2017)
Acknowledgement
This paper is supported by NSFC (No. 61772330, 61533012, 61876109, 61472075, 61876085), the Basic Research Project of Shanghai “Innovation Action Plan” (16JC1402800) and the interdisciplinary Program of Shanghai Jiao Tong University (YG2015MS43).
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Li, W., Chen, Z., Fu, Z., Lu, H. (2019). Multilevel Collaborative Attention Network for Person Search. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11361. Springer, Cham. https://doi.org/10.1007/978-3-030-20887-5_29
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