• Opto-Electronic Engineering
  • Vol. 46, Issue 9, 180606 (2019)
Liu Hui, Peng Li, and Wen Jiwei*
Author Affiliations
  • [in Chinese]
  • show less
    DOI: 10.12086/oee.2019.180606 Cite this Article
    Liu Hui, Peng Li, Wen Jiwei. Multi-occluded pedestrian real-time detection algorithm based on preprocessing R-FCN[J]. Opto-Electronic Engineering, 2019, 46(9): 180606 Copy Citation Text show less
    References

    [1] Dollar P, Wojek C, Schiele B, et al. Pedestrian detection: an Evaluation of the State of the art[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(4): 743–761.

    [2] Wang X Y, Han T X, Yan S C. An HOG-LBP human detector with partial occlusion handling[C]//Proceedings of the 12th IEEE International Conference on Computer Vision, 2009: 32–39.

    [3] Dai J F, Li Y, He K M, et al. R-FCN: object detection via region-based fully convolutional networks[C]//Proceedings of the 30th Conference on Neural Information Processing Systems, 2016: 379–387.

    [4] Wang K J, Zhao Y D, Xing X L. Deep learning in driverless vehicles[J]. CAAI Transactions on Intelligent Systems, 2018, 13(1): 55–69.

    [5] Wang Z L, Huang M, Zhu Q B, et al. The optical flow detection method of moving target using deep convolution neural network[J]. Opto-Electronic Engineering, 2018, 45(8): 180027.

    [6] Liu W, Anguelov D, Erhan D, et al. SSD: single shot MultiBox detector[C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 21–37.

    [7] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015: 91–99.

    [8] Cheng D Q, Tang S X, Feng C C, et al. Extended HOG-CLBC for pedstrain detection[J]. Opto-Electronic Engineering, 2018, 45(8): 180111.

    [9] Ouyang W L, Wang X G. Joint deep learning for pedestrian detection[C]//Proceedings of 2013 IEEE International Conference on Computer Vision, 2014: 2056–2063.

    [10] Tian Y L, Luo P, Wang X G, et al. Deep learning strong parts for pedestrian detection [C]//Proceedings of 2015 IEEE International Conference on Computer Vision, 2015: 1904–1912.

    [11] Ouyang W L, Zeng X Y, Wang X G. Partial occlusion handling in pedestrian detection with a deep model[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2016, 26(11): 2123–2137.

    [12] Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[J]. arXiv:1512.00567v3[cs.CV], 2015.

    [13] Han W, Khorrami P, Le Paine P, et al. Seq-NMS for video object detection[J]. arXiv:1602.08465[cs.CV], 2016.

    [14] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770–778.

    [15] He K M, Gkioxari G, Dollár P, et al. Mask R-CNN[C]// Proceedings of 2017 IEEE International Conference on Computer Vision, 2017: 2980–2988.

    [16] Dai J F, Qi H Z, Xiong Y W, et al. Deformable convolutional networks[C]//Proceedings of 2017 IEEE International Conference on Computer Vision, 2017: 764–773.

    [17] Bell S, Zitnick C L, Bala K, et al. Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 2874–2883.

    [18] Cai Z W, Fan Q F, Feris R S, et al. A unified multi-scale deep convolutional neural network for fast object detection[C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 354–370.

    [19] Goodfellow I J, Warde-Farley D, Mirza M, et al. Maxout networks[J]. JMLR WCP, 2013, 28(3): 1319–1327.

    [20] Zhang L L, Lin L, Liang X D, et al. Is faster R-CNN doing well for pedestrian detection [C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 443–457.

    [21] Tian Y L, Luo P, Wang X G, et al. Pedestrian detection aided by deep learning semantic tasks[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015: 5079–5087.

    [22] Du X Z, El-Khamy M, Lee J, et al. Fused DNN: a deep neural network fusion approach to fast and robust pedestrian detection[C]//Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision, 2017.

    [23] Dollár P, Appel R, Belongie S, et al. Fast feature pyramids for object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(8): 1532–1545.

    [24] Nam W, Dollár P, Han J H. Local decorrelation for improved pedestrian detection[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014: 424–432.

    CLP Journals

    [1] Xue Lixia, Zhu Zhengfa, Wang Ronggui, Yang Juan. Person re-identification by multi-division attention[J]. Opto-Electronic Engineering, 2020, 47(11): 190628

    Liu Hui, Peng Li, Wen Jiwei. Multi-occluded pedestrian real-time detection algorithm based on preprocessing R-FCN[J]. Opto-Electronic Engineering, 2019, 46(9): 180606
    Download Citation