SINDAGI V A, PATEL V M. A Survey of Recent Advances in CNN-Based Single Image Crowd Counting and Density Estimation [J]. Pattern Recognition Letters, 2018, 107: 3-16.
GAO G S, GAO J Y, LIU Q J, et al. CNN-Based Density Estimation and Crowd Counting: A Survey [EB/OL]. (2003-08-06)[2021-12-01]. https://arxiv.org/abs/2003.12783
LIU N, LONG Y C, ZOU C Q, et al. ADCrowdNet: an Attention-Injective Deformable Convolutional Network for Crowd Understanding [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE Computer Society Press, 2019: 3220-3229.
SIMONYAN K, ZISSERMAN A. Very Deep Convolutional Networks for Large-Scale Image Recognition [EB/OL]. (2014-05-06)[2021-12-05]. https://arxiv.org/abs/1409.1556.
LI Y H, ZHANG X F, CHEN D M. CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes [J]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Computer Society Press, 2018: 1091-1100.
ZHAO M M, ZHANG J, ZHANG C Y, et al. Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE Computer Society Press, 2019: 12728-12737.
GAO J Y, WANG Q, LI X L. PCC Net: Perspective Crowd Counting via Spatial Convolutional Network [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(10): 3486-3498.
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. New York: IEEE Computer Society Press, 2016: 589-597.
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. New York: IEEE Computer Society Press, 2013: 2547-2554.
SINDAGI V A, PATEL V M. CNN-Based Cascaded Multi-Task Learning of High-Level Prior and Density Estimation for Crowd Counting [C]//201714th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). New York: IEEE Computer Society Press, 2017.
SINDAGI V A, PATEL V M. Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNS [C]//2017 IEEE International Conference on Computer Vision. New York: IEEE Computer Society Press, 2017: 1879-1888.
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. New York: IEEE Computer Society Press, 2017: 4031-4039.
LIU J, GAO C Q, MENG D Y, et al. DecideNet: Counting Varying Density Crowds through Attention Guided Detection and Density Estimation [J]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Computer Society Press, 2018: 5197-5206.
HUANG S Y, LI X, ZHANG Z F, et al. Body Structure Aware Deep Crowd Counting [J]. IEEE Transactions on Image Processing, 2018, 27(3): 1049-1059. doi: 10.1109/TIP.2017.2740160
SHEN Z, XU Y, NI B B, et al. Crowd Counting via Adversarial Cross-Scale Consistency Pursuit [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Computer Society Press, 2018: 5245-5254.
WANG L Y, YIN B Q, TANG X, et al. Removing Background Interference for Crowd Counting via De-Background Detail Convolutional Network [J]. Neurocomputing, 2019, 332: 360-371. doi: 10.1016/j.neucom.2018.12.047
IDREES H, TAYYAB M, ATHREY K, et al. Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds [EB/OL]. (2018-08-16)[2021-12-05]. http://www.cs.ucf.edu/~haroon/datafiles/Idrees_Counting_ECCV_2018.pdf.