[1] 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, 2017, 107:3-16. [2] FU H, MA H, XIAO H. Scene-adaptive accurate and fast vertical crowd counting via joint using depth and color information[J]. Multimedia Tools and Applications, 2014, 73(1):273-289. [3] LI W, MAHADEVAN V, VASCONCELOS N. Anomaly detection and localization in crowded scenes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(1):18-32. [4] ZHOU B, TANG X, WANG X. Learning collective crowd behaviors with dynamic pedestrian-agents[J]. International Journal of Computer Vision, 2015, 111(1):50-68. [5] CHANDOLA V, BANERJEE A, KUMAR V. Anomaly detection:a survey[J]. ACM Computing Surveys, 2009, 41(3):No.15. [6] KE Y, SUKTHANKAR R, HEBERT M. Event detection in crowded videos[C]//Proceedings of the IEEE 11th International Conference on Computer Vision. Piscataway:IEEE, 2007:1-8. [7] 魏武,张起森,王明俊,等. 基于计算机视觉和图像处理的交通参数检测[J]. 信息与控制, 2001, 30(3):257-261. (WEI W, ZHANG Q S, WANG M J, et al. Detection of traffic parameters based on computer vision and image processing[J]. Information and Control, 2001, 30(3):257-261.) [8] 张桂铭,朱阿兴,杨胜天,等. 基于核密度估计的动物生境适宜度制图方法[J]. 生态学报, 2013, 33(23):7590-7600. (ZHANG G M, ZHU A X, YANG S T, et al. Mapping wildlife habitat suitability using nuclear density estimation[J]. Acta Ecologica Sinica, 2013, 33(23):7590-7600.) [9] FRENCH G, FISHER M H, MACKIEWICZ M, et al. Convolutional neural networks for counting fish in fisheries surveillance video[C]//Proceedings of the 26th British Machine Vision Conference. Durham:BMVA Press, 2015:23-32. [10] HAAR A. Zur Theorie der orthogonalen Funktionensysteme[J]. Mathematische Annalen, 1910, 69(3):331-371. [11] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2005:886-893. [12] OJALA T, PIETIKÄINEN M, MÄENPÄÄ T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7):971-987. [13] SINDAGI V A, PATEL V M. CNN-based cascaded multi-task learning of high-level prior and density estimation for crowd counting[C]//Proceedings of the 14th IEEE International Conference on Advanced Video and Signal-Based Surveillance. Piscataway:IEEE, 2017:1-6. [14] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6):84-90. [15] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2016:770-778. [16] ZHANG C, LI H, WANG X, et al. Cross-scene crowd counting via deep convolutional neural networks[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2015:833-841. [17] ZHANG L, SHI M, CHEN Q. Crowd counting via scale-adaptive convolutional neural network[C]//Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision. Piscataway:IEEE, 2018:1113-1121. [18] SINDAGI V A, PATEL V M. Generating high-quality crowd density maps using contextual pyramid CNNs[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway:IEEE, 2017:1879-1888. [19] ZHANG Y, ZHOU D, CHEN S, et al. Single-image crowd counting via multi-column convolutional neural network[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2016:589-597. [20] ZEILER, M D, FERGUS R. Visualizing and understanding convolutional neural networks[C]//Proceedings of the 2014 European Conference on Computer Vision, LNCS 8689. Cham:Springer, 2014:818-833. [21] KARPATHY A, TODERICI G, SHETTY S, et al. Large-scale video classification with convolutional neural networks[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2014:1725-1732. [22] SAM D B, SURYA S, BABU R V. Switching convolutional neural network for crowd counting[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2017:4031-4039. [23] ZENG L, XU X, CAI B, et al. Multi-scale convolutional neural networks for crowd counting[C]//Proceedings of the 2017 IEEE International Conference on Image Processing. Piscataway:IEEE, 2017:465-469. [24] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2015, 3431-3440. [25] 刘明林. 基于深度学习的人群密度估计及稠密人群计数的研究[D].郑州:郑州大学, 2017:1-55. (LIU M L. Research on crowd density estimation and dense population count based on deep learning[D]. Zhengzhou:Zhengzhou University, 2017:1-55.) [26] WALACH E, WOLF L. Learning to count with CNN boosting[C]//Proceedings of the 2016 European Conference on Computer Vision, LNCS 9906. Cham:Springer, 2016:660-676. [27] MARSDEN M, MACGUINNESS K, LITTLE S, et al. Fully convolutional crowd counting on highly congested scenes[C]//Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Setúbal:SciTePress, 2017:27-33. [28] LI Y, ZHANG X, CHEN D. CSRNet:dilated convolutional neural networks for understanding the highly congested scenes[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2018:1091-1100. [29] LIU J, GAO C, MENG D, et al. DecideNet:counting varying density crowds through attention guided detection and density estimation[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2018:5197-5206. [30] HUANG S, LI X, ZHANG Z, et al. Body structure aware deep crowd counting[J]. IEEE Transactions on Image Processing, 2018, 27(3):1049-1059. |