Abstract
Cross-scene crowd counting plays a more and more important role in intelligent scene monitoring, and it is very important in the safety of personnel and the scene scheduling. The traditional estimation of crowd counting is mainly dependent on the simple background of scenes, which is not conducive to the complex background. To address this problem, in this paper, we propose a multi convolutional kernels net for crowd counting, which discards the subjectivity and the occasionality of the traditional manual feature extraction. Firstly, we label dataset for convolution output features. Then we use the fully convolutional network to create the density map at the end of the network with multi convolutional kernels. Finally, we perform integral regression on density maps to estimate the crowd counting. The dataset that we used is a set of publicly available datasets, which are the Shanghaitech dataset, the UCF_CC_50 dataset and the UCSD dataset. The experiments based on video images show that the proposed method is more effective than traditional methods in terms of robustness and accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, Y., Zhou, D., Chen, S., et al.: Single-Image crowd counting via multi-column convolutional neural network. In: CVPR, pp. 589–597 (2016)
Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: BMVC, vol. 1, p. 3 (2012)
Chan, A.B., Liang, Z.S.J., Vasconcelos, N.: Privacy preserving crowd monitoring: counting people without people models or tracking. In: CVPR, pp. 1–7 (2008)
Brostow, G.J., Cipolla, R.: Unsupervised Bayesian detection of independent motion in crowds. In: CVPR, pp. 594–601 (2006)
Idrees, H., Saleemi, I., Seibert, C., et al.: Multi-source multi-scale counting in extremely dense crowd images. In: CVPR, pp. 2547–2554 (2013)
Zhang, C., Li, H., Wang, X., et al.: Cross-scene crowd counting via deep convolutional neural networks. In: CVPR, pp. 833–841 (2015)
Rodriguez, M., Laptev, I., Sivic, J., et al.: Density-aware person detection and tracking in crowds. In: ICCV, pp. 2423–2430 (2011)
An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: CVPR, pp. 1–7 (2007)
Chen, K., Gong, S., Xiang, T., Loy, C.C.: Cumulative attribute space for age and crowd density estimation. In: CVPR, pp. 2467–2474 (2013)
Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. Int. J. Comput. Vis. 63(2), 153–161 (2005)
Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: Advances in Neural Information Processing Systems, ICONIP, pp. 1324–1332 (2010)
Ojala, T., Pietikäinen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: ICPR, pp. 582–585. IEEE (1994)
Wang, C., Zhang, H., Yang, L., et al. Deep people counting in extremely dense crowds. In: Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, pp. 1299–1302 (2015)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: PAMI, pp. 640–651. IEEE (2017)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, ICONIP. CAI, pp. 1097–1105 (2012)
Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: CVPR, pp. 833–841. IEEE (2015)
Acknowledgments
This work is supported by the National Natural Science Foundation (NSF) of China (No. 61572029, No. 61702001).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Yu, M., Li, T., Zhang, J., Li, J., Yuan, F., Li, R. (2018). Arbitrary Perspective Crowd Counting via Multi Convolutional Kernels. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_52
Download citation
DOI: https://doi.org/10.1007/978-3-030-00767-6_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00766-9
Online ISBN: 978-3-030-00767-6
eBook Packages: Computer ScienceComputer Science (R0)