• Laser & Optoelectronics Progress
  • Vol. 55, Issue 9, 91504 (2018)
Liu Hui, Peng Li, and Wen Jiwei
Author Affiliations
  • [in Chinese]
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    DOI: 10.3788/lop55.091504 Cite this Article Set citation alerts
    Liu Hui, Peng Li, Wen Jiwei. Multi-Scale Aware Pedestrian Detection Algorithm Based on Improved Full Convolutional Network[J]. Laser & Optoelectronics Progress, 2018, 55(9): 91504 Copy Citation Text show less
    References

    [1] Dollár P, Tu Z, Perona P, et al. Integral channel features[C]∥British Machine Vision Conference, 2009: 7-10.

    [2] Nam W, Dollár P, Han J H. Local decorrelation for improved pedestrian detection[C]∥Advances in Neural Information Processing Systems, 2014: 424-432.

    [3] 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.

    [4] Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2005: 886-893.

    [5] Qin J, Wang M H. Fast pedestrian proposal generation algorithm using online Gaussian model[J]. Acta Optica Sinica, 2016, 36(11): 1115001.

    [6] Tian Y, Luo P, Wang X, et al. Pedestrian detection aided by deep learning semantic tasks[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2015: 5079-5087.

    [7] Tian Y, Luo P, Wang X, et al. Deep learning strong parts for pedestrian detection[C]∥IEEE International Conference on Computer Vision, 2015: 1904-1912.

    [8] Ye G L, Sun S Y, Gao K J, et al. Nighttime pedestrian detection based on faster region convolution neural network[J]. Laser & Optoelectronics Progress, 2017, 54(8): 081003.

    [9] Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]∥Advances in Neural Information Processing Systems, 2015: 91-99.

    [10] Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector[C]∥European Conference on Computer Vision, 2016: 21-37.

    [11] Li J, Liang X, Shen S M, et al. Scale-aware fast R-CNN for pedestrian detection[J]. IEEE Transactions on Multimedia, 2017, 20(4): 985-996.

    [12] Cai Z, Fan Q, Feris R S, et al. A unified multi-scale deep convolutional neural network for fast object detection[C]∥European Conference on Computer Vision, 2016: 354-370.

    [13] Hou C C, He Y Q, Jiang X H, et al. Deep convolutional neural network based on two-stream convolutional unit[J]. Laser & Optoelectronics Progress, 2018, 55(2): 021005.

    [14] Girshick R. Fast R-CNN[C]∥IEEE International Conference on Computer Vision, 2015: 1440-1448.

    [15] Dai J, Li Y, He K, et al. R-FCN: object detection via region-based fully convolutional networks[C]∥Advances in Neural Information Processing Systems, 2016: 379-387.

    [16] Bodla N, Singh B, Chellappa R, et al. Soft-NMS-improving object detection with one line of code[C]∥IEEE International Conference on Computer Vision, 2017.

    [17] Luo P, Tian Y, Wang X, et al. Switchable deep network for pedestrian detection[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2014: 899-906.

    [18] Ouyang W, Wang X. Joint deep learning for pedestrian detection[C]∥IEEE International Conference on Computer Vision, 2014: 2056-2063.

    [19] Zhang L, Lin L, Liang X, et al. Is faster R-CNN doing well for pedestrian detection [C]∥European Conference on Computer Vision, 2016: 443-457.

    [20] Yang B, Yan J, Lei Z, et al. Convolutional channel features[C]∥IEEE International Conference on Computer Vision, 2015: 82-90.

    [21] Du X, El-Khamy M, Lee J, et al. Fused DNN: a deep neural network fusion approach to fast and robust pedestrian detection[C]∥IEEE Winter Conference on Applications of Computer Vision, 2017: 953-961.