Abstract
This paper proposes a pedestrian detection and re-identification (re-id) integrated net (I-Net) in an end-to-end learning framework. The I-Net is used in real-world video surveillance scenarios, where the target person needs to be searched in the whole scene videos, and the annotations of pedestrian bounding boxes are unavailable. Comparing to the successful OIM method [31] for joint detection and re-id, we have three distinct contributions. First, we implement a Siamese architecture instead of one stream for an end-to-end training strategy. Second, a novel on-line pairing loss (OLP) with a feature dictionary restricts the positive pairs. Third, hard example priority softmax loss (HEP) with little computation cast is proposed to deal with the online hard example mining. We show our results on CUHK-SYSU and PRW datasets. Our method narrows the gap between detection and re-identification, and achieves a superior performance.
This work is supported by National Natural Science Fund of China (Grant 61771079) and Chongqing Science and Technology Project (cstc2017zdcy-zdzxX0002).
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References
Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. In: Computer Vision and Pattern Recognition, pp. 3908–3916 (2015)
Cao, C., Wang, Y., Kato, J., Zhang, G., Mase, K.: Solving occlusion problem in pedestrian detection by constructing discriminative part layers. In: Applications of Computer Vision, pp. 91–99 (2017)
Chen, S.Z., Guo, C.C., Lai, J.H.: Deep ranking for person re-identification via joint representation learning. IEEE Trans. Image Process. 25(5), 2353–2367 (2016)
Chen, W., Chen, X., Zhang, J., Huang, K.: Beyond triplet loss: a deep quadruplet network for person re-identification (2017)
Chen, W., Chen, X., Zhang, J., Huang, K.: A multi-task deep network for person re-identification (2017)
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: Computer Vision and Pattern Recognition, pp. 1335–1344 (2016)
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)
Dollar, P., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532 (2014)
Dollar, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: British Machine Vision Conference, BMVC 2009, London, UK, 7–10 September 2009, Proceedings (2009)
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(9), 1627–1645 (2010)
Girshick, R.: Fast R-CNN. Computer Science (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACM International Conference on Multimedia, pp. 675–678 (2014)
Li, W., Wang, X.: Locally aligned feature transforms across views. In: Computer Vision and Pattern Recognition, pp. 3594–3601 (2013)
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
Li, X., Zheng, W.S., Wang, X., Xiang, T.: Multi-scale learning for low-resolution person re-identification. In: IEEE International Conference on Computer Vision, pp. 3765–3773 (2015)
Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: Computer Vision and Pattern Recognition, pp. 2197–2206 (2015)
Liu, H., et al.: Neural person search machines (2017)
Liu, J., et al.: Multi-scale triplet CNN for person re-identification. In: ACM on Multimedia Conference, pp. 192–196 (2016)
Nam, W., Dollar, P., Han, J.H.: Local decorrelation for improved pedestrian detection. In: NIPS, pp. 1–9 (2014)
Ouyang, W., Zeng, X., Wang, X.: Modeling mutual visibility relationship in pedestrian detection. In: Computer Vision and Pattern Recognition, pp. 3222–3229 (2013)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)
Roth, P.M., Wohlhart, P., Hirzer, M., Kostinger, M., Bischof, H.: Large scale metric learning from equivalence constraints. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2288–2295 (2012)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering, pp. 815–823 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science (2014)
Song, B., Kamal, A.T., Soto, C., Ding, C., Farrell, J.A., Roychowdhury, A.K.: Tracking and activity recognition through consensus in distributed camera networks. IEEE Trans. Image Process. 19(10), 2564–2579 (2010)
Song, H., Wang, W., Wang, J., Wang, R.: Collaborative deep networks for pedestrian detection. In: IEEE Third International Conference on Multimedia Big Data, pp. 146–153 (2017)
Tian, Y., Luo, P., Wang, X., Tang, X.: Pedestrian detection aided by deep learning semantic tasks, pp. 5079–5087 (2014)
Wang, X.: Intelligent multi-camera video surveillance: a review. Pattern Recognit. Lett. 34, 3–19 (2013)
Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: End-to-end deep learning for person search (2016)
Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: Joint detection and identification feature learning for person search. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Yang, B., Yan, J., Lei, Z., Li, S.Z.: Convolutional channel features. In: IEEE International Conference on Computer Vision, pp. 82–90 (2015)
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
Zhang, S., Benenson, R., Schiele, B.: Filtered channel features for pedestrian detection, pp. 1751–1760 (2015)
Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3586–3593 (2013)
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 (2015)
Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., Tian, Q.: Person re-identification in the wild, pp. 3346–3355 (2016)
Zheng, Z., Zheng, L., Yang, Y.: A discriminatively learned CNN embedding for person re-identification (2016)
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He, Z., Zhang, L. (2019). End-to-End Detection and Re-identification Integrated Net 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 11362. Springer, Cham. https://doi.org/10.1007/978-3-030-20890-5_23
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