[1] SHAO C H, SHAO P C, KUO F M. Stampede events and strategies for crowd management[J]. Journal of Disaster Research, 14, 949-958(2019).

[2] YANG Z Y, WEN J, HUANG K D. A method of pedestrian flow monitoring based on received signal strength[J]. EURASIP Journal on Wireless Communications and Networking, 2022, 2(2022).

[3] KHAN M A, MENOUAR H, HAMILA R. Revisiting crowd counting： state-of-the-art， trends， and future perspectives[J]. Image and Vision Computing, 129, 104597(2023).

[4] LIU J, GAO C Q, MENG D Y et al. DecideNet： counting varying density crowds through attention guided detection and density estimation[C], 5197-5206(2018).

[5] LEMPITSKY V, ZISSERMAN A. Learning to count objects in images[C], 1324-1332(2010).

[6] RANJAN V, SHARMA U, NGUYEN T et al. Learning to count everything[C], 3393-3402(2021).

[7] MA Y M, SANCHEZ V, GUHA T. Fusioncount： efficient crowd counting via multiscale feature fusion[C], 3256-3260(2022).

[8] ZHANG Y Y, ZHOU D S, CHEN S Q et al. Single-image crowd counting via multi-column convolutional neural network[C], 589-597(2016).

[9] LI Y H, ZHANG X F, CHEN D M. CSRNet： dilated convolutional neural networks for understanding the highly congested scenes[C], 1091-1100(2018).

[10] ZHU L, ZHAO Z J, LU C et al. Dual path multi-scale fusion networks with attention for crowd counting[J]. Computing Research Repository, 1-9(2019).

[11] LIU W Z, SALZMANN M. Context-aware crowd counting[C], 5094-5103(2019).

[12] SHEN Z, XU Y, NI B B et al. Crowd counting via adversarial cross-scale consistency pursuit[C], 5245-5254(2018).

[13] 余鹰, 李剑飞, 钱进. 基于多尺度特征融合的抗背景干扰人群计数网络[J]. 模式识别与人工智能, 35, 915-927(2022).

     YU Y, LI J F, QIAN J et al. Anti-background interference crowd counting network based on multi-scale feature fusion[J]. Pattern Recognition and Artificial Intelligence, 35, 915-927(2022).

[14] YU F, KOLTUN V, FUNKHOUSER T. Dilated residual networks[C], 636-644(2017).

[15] DAI F, LIU H, MA Y K et al. Dense scale network for crowd counting[J]. CoRR, 64-72(2021).

[16] WANG J Q, CHEN K, XU R et al. CARAFE： Content-aware ReAssembly of FEatures[C], 3007-3016(2019).

[17] MALLYA A, LAZEBNIK S. PackNet： Adding multiple tasks to a single network by iterative pruning[C], 7765-7773(2018).

[18] MALLYA A, DAVIS D, LAZEBNIK S. Piggyback： adapting a single network to multiple tasks by learning to mask weights[C], 72-88(2018).

[19] SUN K, XIAO B, LIU D et al. Deep high-resolution representation learning for human pose estimation[C], 5686-5696(2019).

[20] IDREES H, TAYYAB M, ATHREY K et al. Composition loss for counting， density map estimation and localization in dense crowds[C], 544-559(2018).

[21] SINDAGI V A, YASARLA R, PATEL V M. JHU-CROWD++： large-scale crowd counting dataset and A benchmark method[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 2594-2609(2022).

[22] WANG B Y, LIU H D, SAMARAS D, Minh H et al. Distribution matching for crowd counting[J]. Advances in Neural Information Processing Systems（NeurIPS）. Vancouver： MIT Press, 33, 1595-1607(2020).

[23] YAN Z Y, LI P Y, WANG B et al. Towards learning multi-domain crowd counting[J]. IEEE Transactions on Circuits and Systems for Video Technology, 33, 6544-6557(2023).

[24] LIANG D K, XU W, ZHU Y Y et al. Focal inverse distance transform maps for crowd localization[J]. IEEE Transactions on Multimedia, 25, 6040-6052(2023).

[25] PENG Z X, CHAN S H G. Single domain generalization for crowd counting[C], 28025-28034(2024).

[26] SHI Z L, METTES P, SNOEK C G M. Focus for free in density-based counting[J]. International Journal of Computer Vision, 132, 2600-2617(2024).

[27] SAM D B, SURYA S, BABU R V. Switching convolutional neural network for crowd counting[C], 4031-4039(2017).

[28] JIANG X L, XIAO Z H, ZHANG B C et al. Crowd counting and density estimation by trellis encoder-decoder networks[C], 6126-6135(2019).

[29] OH M H, OLSEN P, RAMAMURTHY K N. Crowd counting with decomposed uncertainty[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 11799-11806(2020).