Crowd Density Estimation Method Based on Multi-Feature Information Fusion

Yuebo Meng; Xuanrun Chen; Guanghui Liu; Shengjun Xu

doi:10.3788/LOP202158.2010021

[1] Zhao L, He Z H, Cao W M et al. Real-time moving object segmentation and classification from HEVC compressed surveillance video[J]. IEEE Transactions on Circuits and Systems for Video Technology, 28, 1346-1357(2018).

[2] Coşar S, Donatiello G, Bogorny V et al. Toward abnormal trajectory and event detection in video surveillance[J]. IEEE Transactions on Circuits and Systems for Video Technology, 27, 683-695(2017).

[3] Li X, Chen M, Nie F et al. A multiview-based parameter free framework for group detection[J]. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 4147-4153(2017).

[4] Yu C Y, Xu Y, Gou L S et al. Crowd counting based on single-column deep spatiotemporal convolutional neural network[J]. Laser & Optoelectronics Progress, 58, 0810011(2021).

[5] Kilambi P, Ribnick E, Joshi A J et al. Estimating pedestrian counts in groups[J]. Computer Vision and Image Understanding, 110, 43-59(2008).

[6] 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).

[7] Li Y H, Zhang X F, Chen D M. 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 City, UT, USA, 1091-1100(2018).

[8] Zuo J, Ba Y L. Population-depth counting algorithm based on multiscale fusion[J]. Laser & Optoelectronics Progress, 57, 241502(2020).

[9] Zhao J M, Li X D, Li B S. Research on the algorithm of sheep dense counting based on UAV images[J]. Laser & Optoelectronics Progress, 58, 2210013(2021).

[10] Liu J, Gao C Q, Meng D Y et al. DecideNet: counting varying density crowds through attention guided detection and density estimation[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, 5197-5206(2018).

[11] Felzenszwalb P F, Girshick R B, McAllester D. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 1627-1645(2010).

[12] Chan A B, Vasconcelos N. Counting people with low-level features and Bayesian regression[J]. IEEE Transactions on Image Processing, 21, 2160-2177(2012).

[13] Shi Z L, Ye Y D, Wu Y P et al. Crowd counting using rank-based spatial pyramid pooling network[J]. Acta Automatica Sinica, 42, 866-874(2016).

[14] Chen K, Gong S G, Xiang T et al. Cumulative attribute space for age and crowd density estimation[C]. //2013 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2013, Portland, OR, USA., 2467-2474(2013).

[15] Idrees H, Tayyab M, Athrey K et al. Composition loss for counting, density map estimation and localization in dense crowds[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11206, 544-559(2018).

[16] Zhu L, Zhao Z J, Lu C et al. Dual path multi-scale fusion networks with attention for crowd counting[EB/OL]. (2019-02-04)[2021-02-25]. https://arxiv.org/abs/1902.01115

[17] Rodriguez M, Laptev I, Sivic J et al. Density-aware person detection and tracking in crowds[C]. //2011 International Conference on Computer Vision, November 6-13, 2011, Barcelona, Spain., 2423-2430(2011).

[18] Arteta C, Lempitsky V, Noble J A et al. Interactive object counting[M]. Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science, 8691, 504-518(2014).

[19] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[20] Cao Y L, Ming T F, He G et al. Artificial recognition of centrifugal pump cavitation status based on deep learning[J]. Journal of Xi’an Jiaotong University, 51, 165-172(2017).

[21] Chang L, Deng X M, Zhou M Q et al. Convolutional neural networks in image understanding[J]. Acta Automatica Sinica, 42, 1300-1312(2016).

[22] 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, October 13, 2015, Brisbane, Australia, 1299-1302(2015).

[23] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

[24] 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).

[25] Meng Y B, Ji T, Liu G H et al. Encoding-decoding multi-scale convolutional neural network for crowd counting[J]. Journal of Xi’an Jiaotong University, 54, 149-157(2020).

[26] Liu W Z, Salzmann M, Fua P. Context-aware crowd counting[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 5094-5103(2019).

[27] Lempitsky V S, Zisserman A. Learning to count objects in images[C]. //Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, December 6-9, 2010, Vancouver, British Columbia, Canada, 1324-1332(2010).

[28] Wang Q, Gao J Y, Lin W et al. NWPU-crowd: a large-scale benchmark for crowd counting and localization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 2141-2149(2021).

[29] Ding X H, Guo Y C, Ding G G et al. ACNet: strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks[C]. //2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South), 1911-1920(2019).

[30] Wang L J, Wen H L, Qin J W et al. Asymmetric convolution with densely connected networks[J]. International Journal of Computing Science and Mathematics, 12, 274-284(2020).

[31] Liu S T, Huang D, Wang Y H. Receptive field block net for accurate and fast object detection[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11215, 404-419(2018).

[32] Zhang Z L, Zhang X Y, Peng C et al. ExFuse: enhancing feature fusion for semantic segmentation[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11214, 273-288(2018).

[33] 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).

[34] 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).

[35] 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).

[36] 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, 11209, 757-773(2018).

[37] Liu J, Gao C Q, Meng D Y et al. DecideNet: counting varying density crowds through attention guided detection and density estimation[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, 5197-5206(2018).