• Laser & Optoelectronics Progress
  • Vol. 57, Issue 6, 061001 (2020)
Peipei Yuan and Liang Zhang*
Author Affiliations
  • Tianjin Key Laboratory of Advanced Signal and Image Processing, Civil Aviation University of China, Tianjin 300300, China
  • show less
    DOI: 10.3788/LOP57.061001 Cite this Article Set citation alerts
    Peipei Yuan, Liang Zhang. Pedestrian Attribute Recognition Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061001 Copy Citation Text show less
    References

    [1] Wang X, Zheng S F, Yang R et al. -01-22)[2019-04-18]. https:∥arxiv.gg363., site/abs/1901, 07474(2019).

    [2] Zhao J T. Single-image defogging algorithm based on deep learning[J]. Laser & Optoelectronics Progress, 56, 111005(2019).

    [3] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 521, 436-444(2015).

    [4] Li Y P, Wan S R. Multi-label recognition of pedestrian attributes based on deep learning[J]. Chinese Journal of Biomedical Engineering, 37, 423-428(2018).

    [5] Li D W, Chen X T, Huang K Q. Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios. [C]∥2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), November 3-6, 2015, Kuala Lumpur, Malaysia. New York: IEEE, 111-115(2015).

    [6] Li Y N, Huang C, Loy C C et al. Human attribute recognition by deep hierarchical contexts[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 9910, 684-700(2016).

    [7] Liu X H, Zhao H Y, Tian M Q et al. HydraPlus-Net: attentive deep features for pedestrian analysis. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 350-359(2017).

    [8] Wang J Y, Zhu X T, Gong S G et al. Attribute recognition by joint recurrent learning of context and correlation. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 531-540(2017).

    [9] Ling H Y. Pedestrian attribute recognition based on knowledge distillation[J]. Computer Applications and Software, 35, 181-184, 193(2018).

    [10] Song C F, Huang Y, Ouyang W L et al. Mask-guided contrastive attention model for person re-identification. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 1179-1188(2018).

    [11] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

    [12] Li J, Guan Y P. Pedestrian re-identification based on adaptive weight assignment using deep learning for pedestrian attributes[J]. Laser & Optoelectronics Progress, 56, 141003(2019).

    [13] Zhang C, Chen Y. Object detection based on hard examples mining using residual network[J]. Laser & Optoelectronics Progress, 55, 101003(2018).

    [14] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

    [15] Pei L, Liu Y, Gao L. Cloud detection of ZY-3 remote sensing images based on fully convolutional neural network and conditional random field[J]. Laser & Optoelectronics Progress, 56, 102802(2019).

    [16] Guo C C, Yu F Q, Chen Y. Image semantic segmentation based on convolutional neural network feature and improved superpixel matching[J]. Laser & Optoelectronics Progress, 55, 081005(2018).

    [17] Deng Y B, Luo P, Loy C C et al. Pedestrian attribute recognition at far distance. [C]∥Proceedings of the ACM International Conference on Multimedia-MM '14, November 3-7, 2014, Orlando, Florida, USA. New York: ACM, 789-792(2014).

    [18] Li D W, Chen X T, Zhang Z et al. Pose guided deep model for pedestrian attribute recognition in surveillance scenarios. [C]∥2018 IEEE International Conference on Multimedia and Expo (ICME), July 23-27, 2018, San Diego, CA, USA. New York: IEEE, 18163829(2018).

    [19] Li D W, Zhang Z, Chen X T et al. -04-27)[2019-04-18]. https:∥arxiv.gg363., site/abs/1603, 07054(2016).

    [20] Yu K, Leng B, Zhang Z et al. -11-17)[2019-04-18]. https:∥arxiv.gg363., site/abs/1611, 05603(2016).

    [21] Sudowe P, Spitzer H, Leibe B. Person attribute recognition with a jointly-trained holistic CNN model. [C]∥2015 IEEE International Conference on Computer Vision Workshop (ICCVW), December 7-13, 2015, Santiago, Chile. New York: IEEE, 329-337(2015).

    Peipei Yuan, Liang Zhang. Pedestrian Attribute Recognition Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061001
    Download Citation