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access icon free Deep learning-based real-time fine-grained pedestrian recognition using stream processing

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References

    1. 1)
      • 1. Foresti, G.L.: ‘Object recognition and tracking for remote video surveillance’, IEEE Trans. Circuits Syst. Video Technol., 1999, 9, (7), pp. 10451062.
    2. 2)
      • 2. Dollar, P., Wojek, C., Schiele, B., et al: ‘Pedestrian detection: an evaluation of the state of the art’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (4), pp. 743761.
    3. 3)
      • 3. Krause, J., Gebru, T., Deng, J., et al: ‘Learning features and parts for fine-grained recognition’. Int. Conf. on Pattern Recognition, 2014, pp. 2633.
    4. 4)
      • 4. Zhang, N., Donahue, J., Girshick, R., et al: ‘Part-based rcnns for fine-grained category detection’, 2014, 8689, pp. 834849.
    5. 5)
      • 5. Yang, S., Bo, L., Wang, J., et al: ‘Unsupervised template learning for fine-grained object recognition’. Advances in Neural Information Processing Systems, 2012, pp. 31223130.
    6. 6)
      • 6. Liu, W., Anguelov, D., Erhan, D., et al: ‘SSD: single shot multibox detector’. European Conference on Computer Vision. Amsterdam, The Netherlands, 2016, pp. 2137.
    7. 7)
      • 7. Lcun, Y., Bottou, L., Bengio, Y., et al: ‘Gradient-based learning applied to document recognition’, Proc. IEEE, 1998, 86, (11), pp. 22782324.
    8. 8)
      • 8. Liang, X., Xu, C., Shen, X., et al: ‘Human parsing with contextualized convolutional neural network’. IEEE Int. Conf. on Computer Vision, 2015, pp. 13861394.
    9. 9)
      • 9. Zhang, W., Xu, L., Duan, P., et al: ‘A video cloud platform combing online and offline cloud computing technologies’, Pers. Ubiquitous Comput., 2015, 19, (7), pp. 10991110, http://dx.doi.org/10.1007/s00779-015-0879-3.
    10. 10)
      • 10. Zhang, W., Xu, L., Li, Z., et al: ‘A deep-intelligence framework for online video processing’, IEEE Softw., 2016, 33, (2), pp. 4451, http://dx.doi.org/10.1109/MS.2016.31.
    11. 11)
      • 11. Zhang, W., Duan, P., Gong, W., et al: ‘A load-aware pluggable cloud framework for real-time video processing’, IEEE Trans. Ind. Inform., 2016, 12, (6), pp. 21662176, https://doi.org/10.1109/TII.2016.2560802.
    12. 12)
      • 12. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘Imagenet classification with deep convolutional neural networks’. Int. Conf. on Neural Information Processing Systems, 2012, pp. 10971105.
    13. 13)
      • 13. Everingham, M., Van Gool, L., Williams, C.K.I., et al: ‘The PASCAL visual object classes challenge 2012 (VOCNN2012) results’, http://www.pascalnetwork.org/challenges/VOC/voCNN2012/workshop/index.html.
    14. 14)
      • 14. Krause, J., Stark, M., Deng, J., et al: ‘3d object representations for fine-grained categorization’. IEEE Int. Conf. on Computer Vision Workshops, 2013, pp. 554561.
    15. 15)
      • 15. Krause, J.: ‘Fine-grained recognition without part annotations’, IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 2015, pp. 55465555.
    16. 16)
      • 16. Lin, T.Y., Roychowdhury, A., Maji, S.: ‘Bilinear CNN models for fine-grained visual recognition’, IEEE Int. Conf. on Computer Vision, 2015, pp. 14491457.
    17. 17)
      • 17. Angelova, A., Zhu, S.: ‘Efficient object detection and segmentation for fine-grained recognition’. Computer Vision and Pattern Recognition, 2013, pp. 811818.
    18. 18)
      • 18. Weber, M., Stiefelhagen, R., Stiefelhagen, R.: ‘Part-based clothing segmentation for person retrieval’. IEEE Int. Conf. on Advanced Video and Signal-Based Surveillance, 2011, pp. 361366.
    19. 19)
      • 19. Long, J., Shelhamer, E., Darrell, T.: ‘Fully convolutional networks for semantic segmentation’. Computer Vision and Pattern Recognition, 2015, pp. 34313440.
    20. 20)
      • 20. Xia, F., Wang, P., Chen, X., et al: ‘Joint multi-person pose estimation and semantic part segmentation’. IEEE Conf. on Computer Vision and Pattern Recognition, 2017, pp. 60806089.
    21. 21)
      • 21. Wang, P., Shen, X., Lin, Z., et al: ‘Joint object and part segmentation using deep learned potentials’, IEEE International Conference on Computer Vision, Chile, 2015, pp. 15731581.
    22. 22)
      • 22. Xia, F., Zhu, J., Wang, P., et al: ‘Pose-guided human parsing with deep learned features’, Computer Science, 2015.
    23. 23)
      • 23. Redmon, J., Divvala, S., Girshick, R., et al: ‘You only look once: unified, real-time object detection’, Computer Science, 2016, pp. 779788.
    24. 24)
      • 24. Ren, S., He, K., Girshick, R., et al: ‘Faster R-CNN: towards real-time object detection with region proposal networks’. IEEE Trans. Pattern Anal. Mach. Intell., 2016, 39, (6), pp. 11.
    25. 25)
      • 25. Aniello, L., Baldoni, R., Querzoni, L.: ‘Adaptive online scheduling in storm’. Proc. of the 7th ACM Int. Conf. on Distributed Event-Based Systems. ACM, 2013, pp. 207218.
    26. 26)
      • 26. Peng, B., Hosseini, M., Hong, Z., et al: ‘Rstorm: resource-aware scheduling in storm’. Proc. of the 16th Annual Middleware Conf. ACM, 2015, pp. 149161.
    27. 27)
      • 27. Cardellini, V., Grassi, V., Lo Presti, F., et al: ‘Distributed QOS-aware scheduling in storm’. Proc. of the 9th ACM Int. Conf. on Distributed Event-Based Systems. ACM, 2015, pp. 344347.
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