Computer Science ›› 2020, Vol. 47 ›› Issue (4): 150-156.doi: 10.11896/jsjkx.190400034
• Computer Graphics & Multimedia • Previous Articles Next Articles
PENG Xian, PENG Yu-xu, TANG Qiang, SONG Yan-qi
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[1]QU J,SHI Z L,YE Y D.Unbalanced crowd density estimation based on convolutional features[J].Computer Science,2018,45(8):236-241. [2]ZHANG Y,ZHOU D,CHEN S,et al.Single-image crowdcounting via multi-column convolutional neural network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:589-597. [3]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.ACM,2015:1299-1302. [4]LIN S F,CHEN J Y,CHAO H X.Estimation of number of people in crowded scenes using perspective transformation[J].IEEE Transactions on Systems,Man,and Cybernetics-Part A:Systems and Humans,2001,31(6):645-654. [5]DALAL N,TRIGGS B.Histograms of oriented gradients forhuman detection[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005(CVPR 2005).IEEE,2005:886-893. [6]WANG M,WANG X.Automatic adaptation of a generic pedestrian detector to a specific traffic scene[C]//CVPR 2011.IEEE,2011:3401-3408[7]GE W,COLLINS R T.Marked point processes for crowd counting[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2009:2913-2920. [8]IDREES H,SOOMRO K,SHAH M.Detecting humans in dense crowds using locally-consistent scale prior and global occlusion reasoning[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(10):1986-1998. [9]LIN Z,DAVIS L S.Shape-based human detection and segmentation via hierarchical part-template matching[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(4):604-618. [10]CHAN A B,VASCONCELOS N.Bayesian poisson regressionfor crowd counting[C]//2009 IEEE 12th International Conference on Computer Vision.IEEE,2009:545-551. [11]CHEN K,LOY C C,GONG S,et al.Feature mining for localised crowd counting[C]//BMVC.2012:3. [12]LEMPITSKY V,ZISSERMAN A.Learning to count objects in images[C]//Advances in Neural Information Processing Systems.2010:1324-1332. [13]ZHANG Y,ZHOU D,CHEN S,et al.Single-image crowdcounting via multi-column convolutional neural network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:589-597. [14]ZENG L,XU X,CAI B,et al.Multi-scale convolutional neural networks for crowd counting[C]//2017 IEEE International Conference on Image Processing (ICIP).IEEE,2017:465-469. [15]LI Y,ZHANG X,CHEN D.Csrnet:Dilated convolutional neural networks for understanding the highly congested scenes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:1091-1100. [16]CAO X,WANG Z,ZHAO Y,et al.Scale aggregation network for accurate and efficient crowd counting[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:734-750. [17]HUANG S,LI X,CHENG Z Q,et al.Stacked pooling:Improving crowd counting by boosting scale invariance[J].arXiv:1808.07456,2018[18]SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:1-9. [19]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014. [20]NAIR V,HINTON G E.Rectified linear units improve restrictedboltzmann machines[C]//Proceedings of the 27th International Conference on Machine Learning (ICML-10).2010:807-814. [21]YU F,KOLTUN V.Multi-scale context aggregation by dilated convolutions[J].arXiv:1511.07122,2015. [22]CHEN L C,PAPANDREOU G,KOKKINOS I,et al.Deeplab:Semantic image segmentation with deep convolutional nets,atrous convolution,and fully connected crfs[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,40(4):834-848. [23]CHEN L C,PAPANDREOU G,SCHROFF F,et al.Rethinking atrous convolution for semantic image segmentation[J].arXiv:1706.05587,2017. [24]ZEILER M D,KRISHNAN D,TAYLOR G W,et al.Deconvolutional networks[C]//2010 IEEE Computer Society Confe-rence on Computer Vision and Pattern Recognition.IEEE,2010:2528-2535. [25]NOH H,HONG S,HAN B.Learning deconvolution network for semantic segmentation[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:1520-1528. [26]ZHANG L,SHI M,CHEN Q.Crowd counting via scale-adaptive convolutional neural network[C]//2018 IEEE WinterConfe-rence on Applications of Computer Vision (WACV).IEEE,2018:1113-1121. [27]RODRIGUEZ M,LAPTEV I,SIVIC J,et al.Density-aware person detection and tracking in crowds[C]//2011 International Conference on Computer Vision.IEEE,2011:2423-2430. |
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