Abstract
Foreground extraction can help to improve the performance of person re-identification. In this paper, we propose a novel image preprocessing strategy for foreground extraction. First, we propose that the appropriate background information of the images should be increased in the cropped pedestrian images to improve the saliency detection performance. Second, an Adaptive Multi-layer Cellular Automata model (AMCA) is put forward to extract the foregrounds. Experiments on the VIPeR show that AMCA outperforms other existing methods both on saliency detection and re-identification performance. Furthermore, in order to verify the effectiveness of the proposed strategy, we build a new dataset named Looser Cropped Pedestrian Recognition Dataset (LCPeR), which simultaneously provides the compact-cropped images and the loose-cropped images. Experiments on the LCPeR show that the proposed strategy can further improve the saliency detection and re-identification performance.
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Notes
- 1.
The dataset can be downloaded in http://pan.baidu.com/s/1c229w44, password: vbyn.
References
Zheng, L., Yi, Y., Hauptmann, A.G.: Person re-identification: past, present and future. arXiv preprint arXiv:1610.02984 (2016)
Bazzani, L., Cristani, M., Murino, V.: SDALF: modeling human appearance with symmetry-driven accumulation of local features. In: Gong, S., Cristani, M., Yan, S., Loy, C.C. (eds.) Person Re-Identification. ACVPR, pp. 43–69. Springer, Cham (2014). https://doi.org/10.1007/978-1-4471-6296-4_3
Yang, Y., Yang, J., Yan, J., Liao, S., Yi, D., Li, S.Z.: Salient color names for person re-identification. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 536–551. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_35
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)
Koestinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: CVPR, pp. 2288–2295 (2012)
Chen, D., Yuan, Z., Chen, B., Zheng, N.: Similarity learning with spatial constraints for person re-identification. In: CVPR, pp. 1268–1277 (2016)
Nguyen, T.B., Pham, V.P., Le, T.L., Le C.V.: Background removal for improving saliency-based person re-identification. In: KSE, pp. 339–344 (2016)
Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24, 5706–5722 (2015)
Jojic, N., Perina, A., Cristani, M., Murino, V., Frey, B.: Stel component analysis: modeling spatial correlations in image class structure. In: CVPR, pp. 2044–2051 (2009)
Liu, Y., Shao, Y., Sun, F.: Person re-identification based on visual saliency. In: ISDA, pp. 884–889 (2012)
Ma, C., Miao, Z., Li, M.: Saliency preprocessing for person re-identification images. In: ICASSP, pp. 1941–1945 (2016)
Wang, J., Lu, H., Li, X., Tong, N., Liu, W.: Saliency detection via background and foreground seed selection. Neurocomputing 152, 359–368 (2015)
Qin, Y., Lu, H., Xu, Y., Wang, H.: Saliency detection via cellular automata. In: CVPR, pp. 110–119 (2015)
Li, X., Lu, H., Zhang, L., Ruan, X., Yang, M.H.: Saliency detection via dense and sparse reconstruction. In: ICCV, pp. 2976–2983 (2013)
Sun, J., Lu, H., Liu, X.: Saliency region detection based on markov absorption probabilities. IEEE Trans. Image Process. 24, 1639–1649 (2015)
Li, C., Yuan, Y., Cai, W., Xia, Y., Feng, D.D.: Robust saliency detection via regularized random walks ranking. In: CVPR, pp. 2710–2717 (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Neural Information Processing Systems, pp. 91–99 (2015)
Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.M.: Global contrast based salient region detection. IEEE Trans. PAMI 37, 569–582 (2015)
Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11, 23–27 (1975)
Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: PETS, vol. 3 (2007)
Perazzi, F., Krahenbuhl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: CVPR, pp. 733–740 (2012)
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: CVPR, pp. 1597–1604 (2009)
Wang, X., Doretto, G., Sebastian, T., Rittscher, J., Tu, P.: Shape and appearance context modeling. In: ICCV, pp. 1–8 (2007)
Acknowledgement
This work is supported by the Shenzhen Municipal Development and Reform Commission (Disciplinary Development Program for Data Science and Intelligent Computing), and the Shenzhen Engineering Laboratory of Broadband Wireless Network Security.
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Wang, D., Yao, W., Zhu, Y. (2018). A Novel Image Preprocessing Strategy for Foreground Extraction in Person Re-identification. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_16
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