Computer Science ›› 2021, Vol. 48 ›› Issue (6): 118-124.doi: 10.11896/jsjkx.200700107
• Computer Graphics & Multimedia • Previous Articles Next Articles
LI Jia-qian, YAN Hua
CLC Number:
[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,2018,107:3-16. [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]ZENG L,XU X,CAI B,et al.Multi-scale convolutional neuralnetworks for crowd counting[C]//2017 IEEE International Conference on Image Processing(ICIP).IEEE,2017:465-469. [4]JIANG X,XIAO Z,ZHANG B,et al.Crowd counting and density estimation by trellis encoder-decoder networks[C]//Procee-dings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:6133-6142. [5]SINDAGI V A,PATEL V M.Generating high-quality crowddensity maps using contextual pyramid cnns[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:1861-1870. [6]WU B,NEVATIA R.Detection of multiple,partially occludedhumans in a single image by bayesian combination of edgelet part detectors[C]//Tenth IEEE International Conference on Computer Vision (ICCV’05).IEEE,2005,1:90-97. [7]WANG M,WANG X.Automatic adaptation of a generic pedestrian detector to a specific traffic scene[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2011:3401-3408. [8]DOLLAR P,WOJEK C,SCHIELE B,et al.Pedestrian detec-tion:An evaluation of the state of the art[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,34(4):743-761. [9]VIOLA P,JONES M J.Robust real-time face detection[J].International Journal of Computer Vision,2004,57(2):137-154. [10]DALAL N,TRIGGS B.Histograms of oriented gradients forhuman detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR’05).IEEE,2005:886-893. [11]CHAN A B,LIANG Z S J,VASCONCELOS N.Privacy preserving crowd monitoring:Counting people without people models or tracking[C]//2008 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2008:1-7. [12]CHEN K,LOY C C,GONG S,et al.Feature mining for localised crowd counting[C]//British Machine Vision Conference.2012:3. [13]RYAN D,DENMAN S,FOOKES C,et al.Crowd counting using multiple local features[C]//2009 Digital Image Computing:Techniques and Applications.IEEE,2009:81-88. [14]LEMPITSKY V,ZISSERMAN A.Learning to count objects in images[C]//Advances in Neural Information Processing Systems.2010:1324-1332. [15]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.2015:1299-1302. [16]BABU S D,SAJJAN N N,VENKATESH B R,et al.Divide and grow:Capturing huge diversity in crowd images with incrementally growing cnn[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:3618-3626. [17]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. [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]SHEN Z,XU Y,NI B,et al.Crowd counting via adversarial cross-scale consistency pursuit[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:5245-5254. [20]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. [21]SHI M,YANG Z,XU C,et al.Revisiting perspective information for efficient crowd counting[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:7279-7288. [22]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014. [23]LIN T Y,DOLLÁR P,GIRSHICK R,et al.Feature pyramidnetworks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:2117-2125. [24]YU F,KOLTUN V.Multi-scale context aggregation by dilated convolutions[J].arXiv:1511.07122,2015. [25]WANG P,CHEN P,YUAN Y,et al.Understanding convolution for semantic segmentation[C]//2018 IEEE Winter Conference on Applications of Computer Vision(WACV).IEEE,2018:1451-1460. [26]HU J,SHEN L,SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7132-7141. [27]ZHANG Y,ZHOU C,CHANG F,et al.Multi-resolution attention convolutional neural network for crowdcounting[J].Neurocomputing,2019,329:144-152. [28]CHEN J,SU W,WANG Z.Crowd counting with crowd attention convolutional neuralnetwork[J].Neurocomputing,2020,382:210-220. [29]IDREES H,SALEEMI I,SEIBERT C,et al.Multi-source multi-scale counting in extremely dense crowd images[C]//Procee-dings of the IEEE Conference on Computer Vision and Pattern Recognition.2013:2547-2554. [30]ZHANG C,LI H,WANG X,et al.Cross-scene crowd counting via deep convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:833-841. |
[1] | WU Zi-yi, LI Shao-mei, JIANG Meng-han, ZHANG Jian-peng. Ontology Alignment Method Based on Self-attention [J]. Computer Science, 2022, 49(9): 215-220. |
[2] | ZHAO Zheng-peng, LI Jun-gang, PU Yuan-yuan. Low-light Image Enhancement Based on Retinex Theory by Convolutional Neural Network [J]. Computer Science, 2022, 49(6): 199-209. |
[3] | PENG Xian, PENG Yu-xu, TANG Qiang, SONG Yan-qi. Crowd Counting Based on Single-column Multi-scale Convolutional Neural Network [J]. Computer Science, 2020, 47(4): 150-156. |
[4] | LIU Yan, LEI Yin-jie, NING Qian. Study of Crowd Counting Algorithm of “Weak Supervision” Dense Scene Based on DeepNeural Network [J]. Computer Science, 2020, 47(4): 184-188. |
[5] | CHEN Xun-min, YE Shu-han, ZHAN Rui. Crowd Counting Model of Convolutional Neural Network Based on Multi-task Learning and Coarse to Fine [J]. Computer Science, 2020, 47(11A): 183-187. |
[6] | LI Zong-min, LI Si-yuan, LIU Yu-jie, LI Hua. Sketch-based Image Retrieval Based on Attention Model [J]. Computer Science, 2020, 47(11): 199-204. |
[7] | DING Ya-san, GUO Bin, XIN Tong, WANG Pei, WANG Zhu, YU Zhi-wen. WiCount:A Crowd Counting Method Based on WiFi Channel State Information [J]. Computer Science, 2019, 46(11): 297-303. |
[8] | LONG Xing-yan, QU Dan, ZHANG Wen-lin. Attention Based Acoustics Model Combining Bottleneck Feature LONG Xing-yan QU Dan ZHANG Wen-lin [J]. Computer Science, 2019, 46(1): 260-264. |
[9] | LI Yun-bo, TANG Si-qi, ZHOU Xing-yu, PAN Zhi-song. Crowd Counting Method via Scalable Modularized Convolutional Neural Network [J]. Computer Science, 2018, 45(8): 17-21. |
[10] | XU Yang, CHEN Yi, HUANG Lei, XIE Xiao-yao. Crowd Counting Method Based on Multilayer BP Neural Networks and Non-parameter Tuning [J]. Computer Science, 2018, 45(10): 235-239. |
[11] | CHAI Zhen-liang and ZANG Di. Localization of Causing-traffic-trouble Vehicle with Multi-level Cascaded Visual Attention Model [J]. Computer Science, 2015, 42(4): 285-291. |
[12] | ZHOU Ying, ZHANG Ji-hong, LIANG Yong-sheng and LIU Wei. Motion Characteristics Based Video Salient Region Extraction Method [J]. Computer Science, 2015, 42(11): 118-122. |
[13] | XU Hang,ZANG Di,CHENG Cheng and ZHANG Ya-ying. Vehicle Matching Based on Quaternion Visual Attention Model [J]. Computer Science, 2014, 41(6): 269-274. |
|