Ma, T.J.[Tian-Jun],
Ji, Q.G.[Qing-Ge],
Li, N.[Ning],
Scene invariant crowd counting using multi-scales head detection in
video surveillance,
IET-IPR(12), No. 12, December 2018, pp. 2258-2263.
DOI Link
1812
BibRef
Li, H.[He],
Zhang, S.H.[Shi-Hui],
Kong, W.H.[Wei-Hang],
Crowd counting using a self-attention multi-scale cascaded network,
IET-CV(13), No. 6, September 2019, pp. 556-561.
DOI Link
1911
BibRef
Wu, Q.[Qin],
Yan, F.F.[Fang-Fang],
Chai, Z.[Zhilei],
Guo, G.D.[Guo-Dong],
Crowd counting by the dual-branch scale-aware network with ranking loss
constraints,
IET-CV(14), No. 3, April 2020, pp. 101-109.
DOI Link
2003
BibRef
Zhao, M.M.[Mu-Ming],
Zhang, C.Y.[Chong-Yang],
Zhang, J.[Jian],
Porikli, F.M.[Fatih M.],
Ni, B.B.[Bing-Bing],
Zhang, W.J.[Wen-Jun],
Scale-Aware Crowd Counting via Depth-Embedded Convolutional Neural
Networks,
CirSysVideo(30), No. 10, October 2020, pp. 3651-3662.
IEEE DOI
2010
Estimation, Distortion, Cameras, Task analysis,
Convolutional neural networks, Australia, Fuses, Crowd counting,
scale variation
BibRef
Zhu, M.[Ming],
Wang, X.[Xuqing],
Tang, J.[Jun],
Wang, N.[Nian],
Qu, L.[Lei],
Attentive multi-stage convolutional neural network for crowd counting,
PRL(135), 2020, pp. 279-285.
Elsevier DOI
2006
Crowd counting, Density estimation,
Convolutional neural network, Soft attention mechanism
BibRef
Li, H.[He],
Kong, W.H.[Wei-Hang],
Zhang, S.H.[Shi-Hui],
Effective crowd counting using multi-resolution context and image
quality assessment-guided training,
CVIU(201), 2020, pp. 103065.
Elsevier DOI
2011
Crowd counting, Scale variant, Image quality assessment, Multi-resolution
BibRef
Wang, Y.J.[Yong-Jie],
Zhang, W.[Wei],
Huang, D.X.[Dong-Xiao],
Liu, Y.Y.[Yan-Yan],
Zhu, J.H.[Jiang-Hua],
Multi-scale supervised network for crowd counting,
IET-IPR(14), No. 17, 24 December 2020, pp. 4701-4707.
DOI Link
2104
BibRef
Zhou, Y.[Yuan],
Yang, J.X.[Jian-Xing],
Li, H.[Hongru],
Cao, T.[Tao],
Kung, S.Y.[Sun-Yuan],
Adversarial Learning for Multiscale Crowd Counting Under Complex
Scenes,
Cyber(51), No. 11, November 2021, pp. 5423-5432.
IEEE DOI
2112
BibRef
Earlier: A2, A1, A5, Only:
Multi-scale Generative Adversarial Networks for Crowd Counting,
ICPR18(3244-3249)
IEEE DOI
1812
Generators, Feature extraction, Sociology, Statistics, Estimation,
Training, Task analysis, Adversarial learning, crowd counting,
multiscale generator.
Estimation, Convolution, Generative adversarial networks, Training
BibRef
Lei, T.[Tao],
Zhang, D.[Dong],
Wang, R.S.[Ri-Sheng],
Li, S.Y.[Shu-Ying],
Zhang, W.J.[Wei-Jiang],
Nandi, A.K.[Asoke K.],
MFP-Net: Multi-scale feature pyramid network for crowd counting,
IET-IPR(15), No. 14, 2021, pp. 3522-3533.
DOI Link
2112
BibRef
Zeng, X.[Xin],
Guo, Q.[Qiang],
Duan, H.R.[Hao-Ran],
Wu, Y.P.[Yun-Peng],
Multi-level features extraction network with gating mechanism for
crowd counting,
IET-IPR(15), No. 14, 2021, pp. 3534-3542.
DOI Link
2112
BibRef
Zhao, H.Y.[Hao-Yu],
Min, W.D.[Wei-Dong],
Wei, X.[Xin],
Wang, Q.[Qi],
Fu, Q.[Qiyan],
Wei, Z.[Zitai],
MSR-FAN: Multi-scale residual feature-aware network for crowd
counting,
IET-IPR(15), No. 14, 2021, pp. 3512-3521.
DOI Link
2112
BibRef
Xue, Y.,
Li, Y.,
Liu, S.,
Zhang, X.,
Qian, X.,
Crowd Scene Analysis Encounters High Density and Scale Variation,
IP(30), 2021, pp. 2745-2757.
IEEE DOI
2102
Location awareness, Image reconstruction, Image coding,
Task analysis, Training, Compressed sensing, Head, Crowd counting,
crowd localization
BibRef
Liu, L.,
Jiang, J.,
Jia, W.,
Amirgholipour, S.,
Wang, Y.,
Zeibots, M.,
He, X.,
DENet: A Universal Network for Counting Crowd With Varying Densities
and Scales,
MultMed(23), 2021, pp. 1060-1068.
IEEE DOI
2103
Convolution, Estimation, Feature extraction, Loss measurement,
Image segmentation, detection
BibRef
Ji, Q.G.[Qing-Ge],
Chen, H.[Hang],
Bao, D.[Di],
Improving crowd counting with scale-aware convolutional neural
network,
IET-IPR(15), No. 10, 2021, pp. 2192-2201.
DOI Link
2108
BibRef
Xu, C.F.[Chen-Feng],
Liang, D.K.[Ding-Kang],
Xu, Y.C.[Yong-Chao],
Bai, S.[Song],
Zhan, W.[Wei],
Bai, X.[Xiang],
Tomizuka, M.[Masayoshi],
AutoScale: Learning to Scale for Crowd Counting,
IJCV(130), No. 2, February 2022, pp. 405-434.
Springer DOI
2202
BibRef
Zhou, L.F.[Li-Fang],
Wang, P.[Peiwen],
Li, W.S.[Wei-Sheng],
Leng, J.X.[Jia-Xu],
Lei, B.J.[Bang-Jun],
Semantic-refined spatial pyramid network for crowd counting,
PRL(159), 2022, pp. 9-15.
Elsevier DOI
2206
Crowd counting, Multi-scale, Semantic-refined, Convolutional neural network
BibRef
Wu, Z.[Zhe],
Zhang, X.F.[Xin-Feng],
Tian, G.[Geng],
Wang, Y.[Yaowei],
Huang, Q.M.[Qing-Ming],
Spatial-Temporal Graph Network for Video Crowd Counting,
CirSysVideo(33), No. 1, January 2023, pp. 228-241.
IEEE DOI
2301
Computational modeling, Predictive models, Analytical models,
Long short term memory, Optical flow,
multi-scale module
BibRef
Wang, L.[Lin],
Li, J.[Jie],
Zhang, S.Q.[Si-Qi],
Qi, C.[Chun],
Wang, P.[Pan],
Wang, F.P.[Feng-Ping],
Multi-Scale and spatial position-based channel attention network for
crowd counting,
JVCIR(90), 2023, pp. 103718.
Elsevier DOI
2301
Crowd counting, Spatial position-based channel attention model, Adaptive loss
BibRef
Wang, M.J.[Ming-Jie],
Cai, H.[Hao],
Han, X.F.[Xian-Feng],
Zhou, J.[Jun],
Gong, M.L.[Ming-Lun],
STNet: Scale Tree Network With Multi-Level Auxiliator for Crowd
Counting,
MultMed(25), 2023, pp. 2074-2084.
IEEE DOI
2306
Task analysis, Training, Estimation, Feature extraction, Semantics,
Computer science, Cognition, Tree structure, scale enhancer, crowd counting
BibRef
Du, Z.P.[Zhi-Peng],
Shi, M.J.[Miao-Jing],
Deng, J.K.[Jian-Kang],
Zafeiriou, S.P.[Stefanos P.],
Redesigning Multi-Scale Neural Network for Crowd Counting,
IP(32), 2023, pp. 3664-3678.
IEEE DOI
2307
Estimation, Neural networks, Task analysis, Feature extraction,
Computer architecture, Optimization, Deep learning, Crowd counting,
relative local counting
BibRef
Wang, M.J.[Ming-Jie],
Zhou, J.[Jun],
Cai, H.[Hao],
Gong, M.L.[Ming-Lun],
CrowdMLP: Weakly-supervised crowd counting via multi-granularity MLP,
PR(144), 2023, pp. 109830.
Elsevier DOI
2310
Weakly-supervised learning, Crowd counting,
Multi-granularity MLP, Self-supervised proxy task
BibRef
Huo, Z.Q.[Zhan-Qiang],
Wang, Y.[Yanan],
Qiao, Y.X.[Ying-Xu],
Wang, J.[Jing],
Luo, F.[Fen],
Domain adaptive crowd counting via dynamic scale aggregation network,
IET-CV(17), No. 7, 2023, pp. 814-828.
DOI Link
2310
computer vision, image processing
BibRef
Zhu, H.L.[Hui-Lin],
Yuan, J.L.[Jing-Ling],
Zhong, X.[Xian],
Liao, L.[Liang],
Wang, Z.[Zheng],
Find Gold in Sand: Fine-Grained Similarity Mining for Domain-Adaptive
Crowd Counting,
MultMed(26), 2024, pp. 3842-3855.
IEEE DOI
2402
Data mining, Adaptation models, Evidence theory, Data models,
Task analysis, Computational modeling, Synthetic data,
multi-scale similarity
BibRef
Li, L.[Lei],
Dong, Y.[Yuan],
Bai, H.L.[Hong-Liang],
Spatial-related and Scale-aware Network for Crowd Counting,
ICPR21(1-7)
IEEE DOI
2105
Heating systems, Visualization, Convolution, Interference,
Benchmark testing, Pattern recognition, Convolutional neural networks
BibRef
Guo, D.[Dewen],
Feng, J.[Jie],
Zhou, B.F.[Bing-Feng],
VGG-Embedded Adaptive Layer-Normalized Crowd Counting Net with
Scale-Shuffling Modules,
ICPR21(1475-1482)
IEEE DOI
2105
Training, Image quality, Lighting, Benchmark testing,
Real-time systems, Pattern recognition, Security
BibRef
Zhang, Y.[Yani],
Zhao, H.L.[Huai-Lin],
Zhou, F.B.[Fang-Bo],
Zhang, Q.[Qing],
Shi, Y.J.[Yan-Jiao],
Liang, L.J.[Lan-Jun],
Mscanet: Adaptive Multi-scale Context Aggregation Network for Congested
Crowd Counting,
MMMod21(II:1-12).
Springer DOI
2106
BibRef
Thanasutives, P.[Pongpisit],
Fukui, K.I.[Ken-Ichi],
Numao, M.[Masayuki],
Kijsirikul, B.[Boonserm],
Encoder-Decoder Based Convolutional Neural Networks with
Multi-Scale-Aware Modules for Crowd Counting,
ICPR21(2382-2389)
IEEE DOI
2105
Training, Adaptation models, Image segmentation, Surveillance,
Neural networks, Semantics, Computer architecture
BibRef
Sajid, U.[Usman],
Ma, W.[Wenchi],
Wang, G.H.[Guang-Hui],
Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd
Counting,
ICPR21(5790-5797)
IEEE DOI
2105
Measurement, Head, Fuses, Lighting, Estimation, Benchmark testing,
Pattern recognition, Crowd counting, crowd-density, input priors
BibRef
Hossain, M.A.[Mohammad Asiful],
Cannons, K.[Kevin],
Jang, D.[Daesik],
Cuzzolin, F.[Fabio],
Xu, Z.[Zhan],
Video-based Crowd Counting Using a Multi-scale Optical Flow Pyramid
Network,
ACCV20(V:3-20).
Springer DOI
2103
BibRef
Shim, K.,
Byun, J.,
Kim, C.,
Multi-Step Quantization Of A Multi-Scale Network For Crowd Counting,
ICIP20(683-687)
IEEE DOI
2011
Quantization (signal), Training, Decoding, Visualization, Surveillance,
Kernel, Head, Crowd counting, Crowd density estimation, Quantization
BibRef
Xie, Y.,
Lu, Y.,
Wang, S.,
RSANet: Deep Recurrent Scale-Aware Network for Crowd Counting,
ICIP20(1531-1535)
IEEE DOI
2011
Convolution, Image restoration, Training, Task analysis, Decoding,
Robustness, Crowd counting, Recurrent network
BibRef
Xu, C.,
Qiu, K.,
Fu, J.,
Bai, S.,
Xu, Y.,
Bai, X.,
Learn to Scale:
Generating Multipolar Normalized Density Maps for Crowd Counting,
ICCV19(8381-8389)
IEEE DOI
2004
image motion analysis, image resolution,
learning (artificial intelligence),
BibRef
Liu, L.,
Qiu, Z.,
Li, G.,
Liu, S.,
Ouyang, W.,
Lin, L.,
Crowd Counting With Deep Structured Scale Integration Network,
ICCV19(1774-1783)
IEEE DOI
2004
feature extraction, image enhancement, image fusion,
image representation, learning (artificial intelligence), Head
BibRef
Bai, H.,
Wen, S.,
Chan, S.G.,
Crowd Counting on Images with Scale Variation and Isolated Clusters,
VisDrone19(18-27)
IEEE DOI
2004
feature extraction, image classification, image segmentation,
object detection, pattern clustering,
Isolated Clusters
BibRef
Khan, S.D.,
Ullah, H.,
Uzair, M.,
Ullah, M.,
Ullah, R.,
Cheikh, F.A.,
Disam: Density Independent and Scale Aware Model for Crowd Counting
and Localization,
ICIP19(4474-4478)
IEEE DOI
1910
Crowd counting, Convolution networks, Head detection, Classification
BibRef
Zhao, K.,
Liu, B.,
Song, L.,
Li, W.,
Yu, N.,
Cascaded Residual Density Network for Crowd Counting,
ICIP19(2199-2203)
IEEE DOI
1910
Crowd counting, Scale variation, CRD-Net, Local count loss
BibRef
Hossain, M.,
Hosseinzadeh, M.,
Chanda, O.,
Wang, Y.,
Crowd Counting Using Scale-Aware Attention Networks,
WACV19(1280-1288)
IEEE DOI
1904
learning (artificial intelligence), neural net architecture,
object detection, crowded scene, crowd density, crowd counting,
Computational modeling
BibRef
Chen, X.,
Bin, Y.,
Sang, N.,
Gao, C.,
Scale Pyramid Network for Crowd Counting,
WACV19(1941-1950)
IEEE DOI
1904
object detection, pedestrians,
traffic engineering computing, Scale Pyramid Module
BibRef
Sindagi, V.,
Patel, V.,
Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd
Counting,
ICCV19(1002-1012)
IEEE DOI
2004
feature extraction, image classification, image fusion,
multiscale fusion, scale-aware ground-truth density maps, Training
BibRef
Vandoni, J.,
Aldea, E.,
Hégarat-Mascle, S.L.,
Evaluating Crowd Density Estimators Via Their Uncertainty Bounds,
ICIP19(4579-4583)
IEEE DOI
1910
density estimation, crowd counting, multi-scale evaluation, uncertainty bounds
BibRef
Shen, Z.,
Xu, Y.,
Ni, B.,
Wang, M.,
Hu, J.,
Yang, X.,
Crowd Counting via Adversarial Cross-Scale Consistency Pursuit,
CVPR18(5245-5254)
IEEE DOI
1812
Estimation, Feature extraction, Training, Task analysis, Kernel,
Generators, Switches
BibRef
Cao, X.K.[Xin-Kun],
Wang, Z.P.[Zhi-Peng],
Zhao, Y.Y.[Yan-Yun],
Su, F.[Fei],
Scale Aggregation Network for Accurate and Efficient Crowd Counting,
ECCV18(VI: 757-773).
Springer DOI
1810
BibRef
Amirgholipour, S.,
He, X.,
Jia, W.,
Wang, D.,
Zeibots, M.,
A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting,
ICIP18(948-952)
IEEE DOI
1809
Head, Adaptation models, Training, Linguistics, Estimation, Testing,
Australia, Crowd counting, Scale Variation, Adaptive Counting CNN
BibRef
Küchhold, M.,
Simon, M.,
Eiselein, V.,
Sikora, T.,
Scale-Adaptive Real-Time Crowd Detection and Counting for Drone
Images,
ICIP18(943-947)
IEEE DOI
1809
Image segmentation, Drones, Feature extraction, Image resolution,
Cameras, Real-time systems, Kernel, crowd counting, crowd detection,
surveillance
BibRef
Zhang, L.,
Shi, M.,
Chen, Q.,
Crowd Counting via Scale-Adaptive Convolutional Neural Network,
WACV18(1113-1121)
IEEE DOI
1806
feature extraction, image classification,
learning (artificial intelligence), neural nets,
Training
BibRef
Siva, P.,
Shafiee, M.J.,
Jamieson, M.,
Wong, A.,
Real-Time, Embedded Scene Invariant Crowd Counting Using
Scale-Normalized Histogram of Moving Gradients (HoMG),
ECVW16(885-892)
IEEE DOI
1612
BibRef
Cao, J.M.[Jin-Meng],
Yang, B.[Biao],
Zhang, Y.Y.[Yu-Yu],
Zou, L.[Ling],
Crowd Counting from a Still Image Using Multi-scale Fully Convolutional
Network with Adaptive Human-Shaped Kernel,
PSIVTWS17(227-240).
Springer DOI
1806
BibRef
Zeng, L.,
Xu, X.,
Cai, B.,
Qiu, S.,
Zhang, T.,
Multi-scale convolutional neural networks for crowd counting,
ICIP17(465-469)
IEEE DOI
1803
Convolutional neural networks, Feature extraction, Kernel,
Optimization, Robustness, Training, Multi-scale CNN, crowd counting,
scale-relevant architectures
BibRef
Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Multi-Modal Crowd Counting .