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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2019, Vol. 13 Issue (3) : 579-587    https://doi.org/10.1007/s11704-017-6598-3
RESEARCH ARTICLE
Crowd counting via learning perspective for multi-scale multi-view Web images
Chong SHANG1, Haizhou AI1(), Yi YANG2
1. Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
2. Huawei Technologies, Beijing 100084, China
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Abstract

Estimating the number of people in Web images still remains a challenging problem owing to the perspective variation, different views, and diverse backgrounds. Existing deep learning models still have difficulties in dealing with scenarios where the size of a person is either extremely large or extremely small. In this paper, we propose a novel perspective-aware architecture to estimate the number of people in a crowd in web images. Specifically,we use a two-stage framework, where we first learn a policy network to infer the perspective of the target scene, which outputs a scale label for the subsequent perspective normalization. Next, given the aligned inputs, we further adjust the scale-specific counting network to regress the final count. Experiments on challenging datasets demonstrate our approach can deal with a large perspective variation and that we have achieved state-of-theart results.

Keywords crowd counting      Web images      perspective inference     
Corresponding Author(s): Haizhou AI   
Just Accepted Date: 13 June 2017   Online First Date: 06 July 2018    Issue Date: 24 April 2019
 Cite this article:   
Chong SHANG,Haizhou AI,Yi YANG. Crowd counting via learning perspective for multi-scale multi-view Web images[J]. Front. Comput. Sci., 2019, 13(3): 579-587.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-017-6598-3
https://academic.hep.com.cn/fcs/EN/Y2019/V13/I3/579
1 SAli, MShah. A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2007
https://doi.org/10.1109/CVPR.2007.382977
2 JShao, KKang, CChange Loy, XWang. Deeply learned attributes for crowded scene understanding. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015, 4657–4666
https://doi.org/10.1109/CVPR.2015.7299097
3 HIdrees, KSoomro, MShah. Detecting humans in dense crowds using locally-consistent scale prior and global occlusion reasoning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(10): 1986–1998
https://doi.org/10.1109/TPAMI.2015.2396051
4 VLempitsky, A Zisserman. Learning to count objects in images. In: Proceedings of the Neural Information Processing Systems Conference. 2010, 1324–1332
5 A BChan, Z S JLiang, NVasconcelos. Privacy preserving crowd monitoring: counting people without people models or tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2008
https://doi.org/10.1109/CVPR.2008.4587569
6 HIdrees, I Saleemi, CSeibert, MShah. Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2547–2554
https://doi.org/10.1109/CVPR.2013.329
7 ZMa, A BChan. Crossing the line: crowd counting by integer programming with local features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2539–2546
https://doi.org/10.1109/CVPR.2013.328
8 C CLoy, SGong, TXiang. From semisupervised to transfer counting of crowds. In: Proceedings of IEEE International Conference on Computer Vision. 2013, 2256–2263
9 KChen, SGong, TXiang, C C Loy. Cumulative attribute space for age and crowd density estimation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2467–2474
https://doi.org/10.1109/CVPR.2013.319
10 LFiaschi, U Köthe, RNair, F AHamprecht. Learning to count with regression forest and structured labels. In: Proceedings of the 21st IEEE International Conference on Pattern Recognition. 2012, 2685–2688
11 KChen, C CLoy, SGong, T Xiang. Feature mining for localised crowd counting. In: Proceedings of the British Machine Vision Conference. 2012
https://doi.org/10.5244/C.26.21
12 CShang, HAi, BBai. End-to-end crowd counting via joint learning local and global count. In: Proceedings of the International Conference on Image Processing. 2016, 1215–1219
https://doi.org/10.1109/ICIP.2016.7532551
13 YZhang, DZhou, SChen, S Gao, YMa. Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2016, 589–597
https://doi.org/10.1109/CVPR.2016.70
14 DOnoro-Rubio, R J López-Sastre. Towards perspective-free object counting with deep learning. In: Proceedings of the European Conference on Computer Vision. 2016, 615–629
https://doi.org/10.1007/978-3-319-46478-7_38
15 CZhang, HLi, XWang, X Yang. Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015, 833–841
https://doi.org/10.1109/CVPR.2015.7298684
16 VRabaud, S Belongie. Counting crowded moving objects. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2006, 705–711
https://doi.org/10.1109/CVPR.2006.92
17 XWu, GLiang, K KLee, Y Xu. Crowd density estimation using texture analysis and learning. In: Proceedings of IEEE International Conference on Robotics and Biomimetics. 2006, 214–219
https://doi.org/10.1109/ROBIO.2006.340379
18 DKong, DGray, HTao. A viewpoint invariant approach for crowd counting. In: Proceedings of the 18th IEEE International Conference on Pattern Recognition. 2006, 1187–1190
https://doi.org/10.1109/ICPR.2006.197
19 YCong, HGong, S CZhu, Y Tang. Flow mosaicking: real-time pedestrian counting without scene-specific learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2009, 1093–1100
https://doi.org/10.1109/CVPR.2009.5206648
20 N CTang, Y Y,Lin M FWeng, H Y M Liao. Cross-camera knowledge transfer for multiview people counting. IEEE Transactions on Image Processing, 2015, 24(1): 80–93
https://doi.org/10.1109/TIP.2014.2363445
21 ZZhang, MWang, XGeng. Crowd counting in public video surveillance by label distribution learning. Elsevier Neurocomputing, 2015, 166: 151–163
https://doi.org/10.1016/j.neucom.2015.03.083
22 BLiu, N Vasconcelos. Bayesian model adaptation for crowd counts. In: Proceedings of IEEE International Conference on Computer Vision. 2015, 4175–4183
https://doi.org/10.1109/ICCV.2015.475
23 CArteta, V Lempitsky, J ANoble, AZisserman. Interactive object counting. In: Proceedings of the European Conference on Computer Vision. 2014, 504–518
https://doi.org/10.1007/978-3-319-10578-9_33
24 V QPham, T Kozakaya, OYamaguchi, ROkada. Count forest: covoting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision. 2015, 3253–3261
https://doi.org/10.1109/ICCV.2015.372
25 P FFelzenszwalb, D P. HuttenlocherEfficient belief propagation for early vision. International Journal of Computer Vision, 2006, 70(1): 41–54
https://doi.org/10.1007/s11263-006-7899-4
26 CSzegedy, WLiu, YJia, P Sermanet, SReed, DAnguelov, DErhan, VVanhoucke, A Rabinovich. Going deeper with convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015
https://doi.org/10.1109/CVPR.2015.7298594
27 KHe, XZhang, SRen, J Sun. Deep residual learning for image recognition. 2015, arXiv preprint arXiv:1512.03385
28 KSimonyan, A Zisserman. Very deep convolutional networks for large-scale image recognition. 2014, arXiv preprint arXiv:1409.1556
29 DKingma, JBa. Adam: a method for stochastic optimization. 2014, arXiv preprint arXiv:1412.6980
30 MRodriguez, JSivic, ILaptev, J Y Audibert. Data-driven crowd analysis in videos. In: Proceedings of IEEE International Conference on Computer Vision. 2011, 1235–1242
https://doi.org/10.1109/ICCV.2011.6126374
31 SAn, WLiu, SVenkatesh. Face recognition using kernel ridge regression. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2007
https://doi.org/10.1109/CVPR.2007.383105
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