[1] [1] SONG Diping, QIAO Yu, CORBETTA A. Depth driven people counting using deep region proposal network[C]// 2017 IEEE International Conference on Information and Automation(ICIA). Macao,China:IEEE, 2017:416-421. doi:10.1109/ICInfA.2017.8078944.

[2] [2] REDMON J,DIVVALA S,GIRSHICK R,et al. You Only Look Once:unified,real-time object detection[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,NV,USA:IEEE, 2016:779-788.

[3] [3] LIU Wei,ANGUELOV D,ERHAN D,et al. SSD:Single Shot multibox Detector[C]// European Conference on Computer Vision. Amsterdam:Springer,Cham, 2016:21-37. doi:10.1007/978-3-319-46448-0_2.

[4] [4] ZHANG Yingying,ZHOU Desen,CHEN Siqin,et al. Single-image crowd counting via multi-column convolutional neural network[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,NV,USA:IEEE, 2016:589-597.

[5] [5] CHAN A B,LIANG Z S J,VASCONCELOS N. Privacy preserving crowd monitoring:counting people without people models or tracking[C]// 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage,AK,USA:IEEE, 2008:1-7.

[6] [6] CHAN A B, VASCONCELOS N. Bayesian Poisson regression for crowd counting[C]// 2009 IEEE the 12th International Conference on Computer Vision. Kyoto,Japan:IEEE, 2009:545-551. doi:10.1109/ICCV.2009.5459191.

[7] [7] ZHANG Cong,LI Hongsheng,WANG Xiaogang,et al. Cross-scene crowd counting via deep convolutional neural networks[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. Boston,MA:IEEE, 2015:833-841. doi:10.1109/ CVPR.2015.7298684.

[8] [8] SINDAGI V A,PATEL V M. Generating high-quality crowd density maps using contextual pyramid CNNs[C]// Proceedings of the IEEE International Conference on Computer Vision. Venice,Italy:IEEE, 2017:1861-1870. doi:10.1109/iccv.2017.206.

[9] [9] CAO Xinkun, WANG Zhipeng, ZHAO Yanyun, et al. Scale aggregation network for accurate and efficient crowd counting[C]// Proceedings of the European Conference on Computer Vision(ECCV). Berlin, Germany: Springer, 2018: 734-750. doi: 10.1007/ 978-3-030-01228-1_45.

[10] [10] ZHANG Lu, SHI Miaojing, CHEN Qiaobo. Crowd counting via scale-adaptive convolutional neural network[C]// 2018 IEEE Winter Conference on Applications of Computer Vision(WACV). Lake Tahoe, NV, USA: IEEE, 2018: 1113-1121. doi: 10.1109/ WACV.2018.00127.

[11] [11] LI Yuhong, ZHANG Xiaofan, CHEN Deming. CSRNet: dilated convolutional neural networks for understanding the highly congested scenes[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA:IEEE, 2018:1091-1100. doi:10.1109/CVPR.2018.00120.

[12] [12] LIU Weizhe, SALZMANN Mathieu, FUA Pascal. Context-aware crowd counting[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Long Beach,CA,USA:IEEE, 2019:5094-5103. doi:10.1109/CVPR.2019.00524.

[13] [13] LIU Ning, LONG Yongchao, ZOU Changqing, et al. ADCrowdNet: an attention-injective deformable convolutional network for crowd understanding[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Long Beach,CA, USA:IEEE, 2019:3220-3229. doi:10.1109/CVPR.2019.00334.

[15] [15] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J/OL]. arXiv: 1409.1556, 2014.

[16] [16] SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going deeper with convolutions[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston,MA,USA:IEEE, 2015:1-9. doi:10.1109/CVPR.2015.7298594.