[1] Luo H L. Study on crowd counting and density estimation based on depth convolution neural network[D]. Chongqing: Chongqing University, 10-11(2018).

[2] Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]∥2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), June 20-25, 2005, San Diego, CA, USA., 886-893(2005).

[3] Viola P, Jones M J. Robust real-time face detection[J]. International Journal of Computer Vision, 57, 137-154(2004). http://www.emeraldinsight.com/servlet/linkout?suffix=b47&dbid=16&doi=10.1108%2F17415651211258272&key=10.1023%2FB%3AVISI.0000013087.49260.fb

[4] Li M, Zhang Z X, Huang K Q et al. Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection[C]∥2008 19th International Conference on Pattern Recognition, December 8-11, 2008, Ta(2008).

[5] Paragios N, Ramesh V. AMRF-based approach for real-time subway monitoring[C]∥Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, December 8-14, 2001, Kauai, HI, USA.(2001).

[6] Johnson J, Alahi A, Li F F. Perceptual losses for real-time style transfer and super-resolution[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 9906, 694-711(2016).

[7] Chen K, Loy C C, Gong S G et al. Feature mining for localised crowd counting[C]∥Procedings British Machine Vision Conference, September 3-7, 2012, Surrey., 21(2012).

[8] Wang C, Zhang H, Yang L et al. Deep people counting in extremely dense crowds[C]∥Proceedings of the 23rd ACM International Conference on Multimedia-MM'15, October 13-30, 2015, Brisbane, Australia., 1299-1302(2015).

[9] Zhang Y Y, Zhou D S, Chen S Q et al. Single-image crowd counting via multi-column convolutional neural network[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 589-597(2016).

[10] [10] Li YH, Zhang XF, Chen DM. CSRNet: dilated convolutional neural networks for understanding the highly congested scenes[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake, USA. New York: IEEE Press, 2018: 1091- 1100.

[11] Cao X K, Wang Z P, Zhao Y Y et al. Scale aggregation network for accurate and efficient crowd counting[M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 11209, 757-773(2018).

[12] Szegedy C, Liu W, Jia Y et al. Going deeper with convolutions[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA.(2015).

[13] WangY J, Hu S Y, Wang G D et al. Multi-scale dilated convolution of convolutional neural network for crowd counting[J]. Multimedia Tools and Applications, 79, 1057-1073(2020). http://link.springer.com/article/10.1007/s11042-019-08208-6

[14] Gao L, Song W D, Tan H et al. Cloud detection based on multi-scale dilation convolutional neural network for ZY-3 satellite remote sensing imagery[J]. Acta Optica Sinica, 39, 0104002(2019).

[15] Yu F. -04-30)[2020-04-29]. https:∥arxiv., org/abs/1511, 07122(2016).

[16] Lin Z J, Luo Z, Zhao L et al. Multi-scale convolution target detection algorithm with feature pyramid[J]. Journal of Zhejiang University (Engineering Science), 53, 533-540(2019).

[17] Idrees H, Saleemi I, Seibert C et al. Multi-source multi-scale counting in extremely dense crowd images[C]∥2013 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2013, Portland, OR, USA., 2547-2554(2013).

[18] Zhang C, Li H S, Wang X G et al. Cross-scene crowd counting via deep convolutional neural networks[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 833-841(2015).

[19] [19] GuoD, LiK, Zha ZJ, et al.DADNet: dilated-attention-deformable ConvNet for crowd counting[C]∥Proceedings of the 27th ACM International Conference on Multimedia, October 21-25, Nice, France. New York: ACM Press, 2019: 1823- 1832.

[20] Sam D B, Surya S, Babu R V. Switching convolutional neural network for crowd counting[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 4031-4039(2017).

[21] Zeng L K, Xu X M, Cai B L et al. Multi-scale convolutional neural networks for crowd counting[C]∥2017 IEEE International Conference on Image Processing (ICIP), September 17-20, 2017, Beijing, China., 465-469(2017).

[22] Marsden M, McGuinness K, Little S et al. -01-17)[2020-04-29]. https:∥arxiv., org/abs/1612, 00220(2017).

[23] Sindagi V A, Patel V M. Generating high-quality crowd density maps using contextual pyramid CNNs[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 1879-1888(2017).

[24] Wan J, Luo W H, Wu B Y et al. Residual regression with semantic prior for crowd counting[C]∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), June 15-20, 2019, Long Beach, CA, USA., 4031-4040(2019).