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2018 | OriginalPaper | Chapter

Regional Estimation Prior Network for Crowd Analyzing

Authors : Ping He, Meng Ma, Ping Wang

Published in: Smart Computing and Communication

Publisher: Springer International Publishing

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Abstract

Crowd analysis from images or videos is an important technology for public safety. CNN-based multi-column methods are widely used in this area. Multi-column methods can enhance the ability of exacting various-scale features for the networks, but they may introduce the drawbacks of complicating and functional redundancy. To deal with this problem, we proposed a multi-task and multi-column network. With the support of a regional estimation prior task, components of network may pay more attention to their own target functions respectively. In this way, the functional redundancy can be reduced and the performance of network can be enhanced. Finally, we evaluated our method in public datasets and monitoring videos.

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Literature
1.
go back to reference Tota, K., Idrees, H.: Counting in dense crowds using deep features. CRCV (2015) Tota, K., Idrees, H.: Counting in dense crowds using deep features. CRCV (2015)
2.
go back to reference Sindagi, V.A., Patel, V.M.: A survey of recent advances in cnn-based single image crowd counting and density estimation. Pattern Recogn. Lett. 107, 3–16 (2018)CrossRef Sindagi, V.A., Patel, V.M.: A survey of recent advances in cnn-based single image crowd counting and density estimation. Pattern Recogn. Lett. 107, 3–16 (2018)CrossRef
3.
go back to reference Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013) Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554 (2013)
4.
go back to reference Liu, J., Gao, C., Meng, D., Hauptmann, A.G.: Decidenet: counting varying density crowds through attention guided detection and density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197–5206 (2018) Liu, J., Gao, C., Meng, D., Hauptmann, A.G.: Decidenet: counting varying density crowds through attention guided detection and density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197–5206 (2018)
5.
go back to reference Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 589–597 (2016) Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 589–597 (2016)
6.
go back to reference Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017) Babu Sam, D., Surya, S., Venkatesh Babu, R.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5744–5752 (2017)
7.
go back to reference Li, Y., Zhang, X., Chen, D.: CSRNet: dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018) Li, Y., Zhang, X., Chen, D.: CSRNet: dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018)
8.
go back to reference Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010)CrossRef Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010)CrossRef
9.
go back to reference Topkaya, I.S., Erdogan, H., Porikli, F.: Counting people by clustering person detector outputs. In: Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 313–318 (2014) Topkaya, I.S., Erdogan, H., Porikli, F.: Counting people by clustering person detector outputs. In: Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 313–318 (2014)
10.
go back to reference Li, M., Zhang, Z., Huang, K., Tan, T.: Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection. In: Proceedings of the IEEE International Conference on Pattern Recognition, pp. 1–4 (2008) Li, M., Zhang, Z., Huang, K., Tan, T.: Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection. In: Proceedings of the IEEE International Conference on Pattern Recognition, pp. 1–4 (2008)
11.
go back to reference Babu Sam, D., Sajjan, N.N., Venkatesh Babu, R., Srinivasan, M.: Divide and grow: capturing huge diversity in crowd images with incrementally growing CNN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3618–3626 (2018) Babu Sam, D., Sajjan, N.N., Venkatesh Babu, R., Srinivasan, M.: Divide and grow: capturing huge diversity in crowd images with incrementally growing CNN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3618–3626 (2018)
12.
go back to reference Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 545–551 (2009) Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 545–551 (2009)
13.
go back to reference Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013) Chen, K., Gong, S., Xiang, T., Change Loy, C.: Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013)
14.
go back to reference Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 1324–1332 (2010) Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 1324–1332 (2010)
15.
go back to reference Wang, C., Zhang, H., Yang, L., Liu, S., Cao, X.: Deep people counting in extremely dense crowds. In: Proceedings of the ACM International Conference on Multimedia, pp. 1299–1302 (2015) Wang, C., Zhang, H., Yang, L., Liu, S., Cao, X.: Deep people counting in extremely dense crowds. In: Proceedings of the ACM International Conference on Multimedia, pp. 1299–1302 (2015)
16.
go back to reference Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 833–841 (2015) Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 833–841 (2015)
17.
go back to reference Onoro-Rubio, D., López-Sastre, R.J.: Towards perspective-free object counting with deep learning. In: proceedings of the European Conference on Computer Vision, pp. 615–629 (2016) Onoro-Rubio, D., López-Sastre, R.J.: Towards perspective-free object counting with deep learning. In: proceedings of the European Conference on Computer Vision, pp. 615–629 (2016)
18.
go back to reference Walach, E., Wolf, L.: Learning to count with CNN boosting. In: Proceedings of the European Conference on Computer Vision, pp. 660–676 (2016) Walach, E., Wolf, L.: Learning to count with CNN boosting. In: Proceedings of the European Conference on Computer Vision, pp. 660–676 (2016)
19.
go back to reference Sindagi, V.A., Patel, V.M.: CNN-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 1–6 (2017) Sindagi, V.A., Patel, V.M.: CNN-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 1–6 (2017)
20.
go back to reference Chen, J.C., Kumar, A., Ranjan, R., Patel, V.M., Alavi, A., Chellappa, R.: A cascaded convolutional neural network for age estimation of unconstrained faces. In: proceedings of the IEEE International Conference on Biometrics Theory, Applications and Systems, pp. 1–8 (2016) Chen, J.C., Kumar, A., Ranjan, R., Patel, V.M., Alavi, A., Chellappa, R.: A cascaded convolutional neural network for age estimation of unconstrained faces. In: proceedings of the IEEE International Conference on Biometrics Theory, Applications and Systems, pp. 1–8 (2016)
Metadata
Title
Regional Estimation Prior Network for Crowd Analyzing
Authors
Ping He
Meng Ma
Ping Wang
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-030-05755-8_25

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