Abstract
In this paper, we construct a multi-task deep learning model to simultaneously predict people number and the level of crowd density. Motivated by the success of applying “ambiguous labelling” to age estimation problem, we also manage to employ this strategy to the people counting problem. We show that it is a reasonable strategy since people counting problem is similar to the age estimation problem. Also, by applying “ambiguous labelling”, we are able to augment the size of training dataset, which is a desirable property when applying to deep learning model. In a series of experiment, we show that the “ambiguous labelling” strategy can not only improve the performance of deep learning but also enhance the prediction ability of traditional computer vision methods such as Random Projection Forest with hand-crafted features.
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References
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D., Li, W.: Deep reconstruction-classification networks for unsupervised domain adaptation. In: European Conference on Computer Vision, pp. 597–613. Springer (2016)
Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings CVPR (2015)
Wang, C., Zhang, H., Yang, L., Liu, S., Cao, X.: Deep people counting in extremely dense crowds. In: Proceedings of the 23rd ACM international conference on Multimedia, pp. 1299–1302. ACM (2015)
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)
Onoro-Rubio, D., López-Sastre, R.J.: Towards perspective-free object counting with deep learning. In: European Conference on Computer Vision, pp. 615–629. Springer (2016)
Sindagi, V.A., Patel, V.M.: CNN-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. arXiv preprint arXiv:1707.09605 (2017)
Chen, K., Kämäräinen, J.-K.: Learning with ambiguous label distribution for apparent age estimation. In: Asian Conference on Computer Vision, pp. 330–343. Springer (2016)
Geng, X., Wang, Q., Xia, Y.: Facial age estimation by adaptive label distribution learning. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 4465–4470. IEEE (2014)
Gao, B.-B., Xing, C., Xie, C.-W., Wu, J., Geng, X.: Deep label distribution learning with label ambiguity. IEEE Trans. Image Process. 26(6), 2825–2838 (2017)
Xu, B., Qiu, G.: Crowd density estimation based on rich features and random projection forest. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–8. IEEE (2016)
Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: Ninth IEEE International Conference on Computer Vision, Proceedings, pp. 734–741. IEEE (2003)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
Zhao, T., Nevatia, R.: Bayesian human segmentation in crowded situations. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings, vol. 2, pp. II–459. IEEE (2003)
Wu, B., Nevatia, R.: Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 1, pp. 90–97. IEEE (2005)
Lin, S.-F., Chen, J.-Y., Chao, H.-X.: Estimation of number of people in crowded scenes using perspective transformation. IEEE Trans. Syst., Man Cybern., Part A: Syst. Hum.S 31(6), 645–654 (2001)
Brostow, G.J., Cipolla, R.: Unsupervised bayesian detection of independent motion in crowds. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 594–601. IEEE (2006)
Rabaud, V., Belongie, S.: Counting crowded moving objects. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 705–711. IEEE (2006)
Foroughi, H., Ray, N., Zhang, H.: Robust people counting using sparse representation and random projection. Pattern Recogn. (2015)
Paragios, N., Ramesh, V.: A MRF-based approach for real-time subway monitoring. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. I–1034. IEEE (2001)
Cho, S.-Y., Chow, T.W., Leung, C.-T.: A neural-based crowd estimation by hybrid global learning algorithm. IEEE Trans. Syst., Man, Cybern., Part B: Cybern. 29(4), 535–541 (1999)
Davies, A.C., Yin, J.H., Velastin, S.A.: Crowd monitoring using image processing. Electron. Commun. Eng. J. 7(1), 37–47 (1995)
Regazzoni, C.S., Tesei, A.: Distributed data fusion for real-time crowding estimation. Signal Process. 53(1), 47–63 (1996)
Marana, A., da Costa, L., Lotufo, R., Velastin, S.: On the efficacy of texture analysis for crowd monitoring. In: International Symposium on Computer Graphics, Image Processing, and Vision, Proceedings, SIBGRAPI 1998, pp. 354–361. IEEE (1998)
Chan, A.B., Liang, Z.-S., Vasconcelos, N.: Privacy preserving crowd monitoring: Counting people without people models or tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–7. IEEE (2008)
Marsden, M., McGuiness, K., Little, S., O’Connor, N.E.: Fully convolutional crowd counting on highly congested scenes. arXiv preprint arXiv:1612.00220 (2016)
Boominathan, L., Kruthiventi, S.S., Babu, R.V.: Crowdnet: a deep convolutional network for dense crowd counting. In: Proceedings of the 2016 ACM on Multimedia Conference, pp. 640–644. ACM (2016)
Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: BMVC, vol. 1, no. 2, p. 3 (2012)
Chen, K., Gong, S., Xiang, T., Loy, C.C.: Cumulative attribute space for age and crowd density estimation. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2467–2474. IEEE (2013)
Kumagai, S., Hotta, K., Kurita, T.: Mixture of counting cnns: Adaptive integration of cnns specialized to specific appearance for crowd counting. arXiv preprint arXiv:1703.09393 (2017)
Sam, D.B., Surya, S., Babu, R.V.: Switching convolutional neural network for crowd counting. arXiv preprint arXiv:1708.00199 (2017)
Sheng, B., Shen, C., Lin, G., Li, J., Yang, W., Sun, C.: Crowd counting via weighted VLAD on dense attribute feature maps. IEEE Trans. Circuits Syst. Video Technol. (2016)
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Xu, B., Zou, W., Garibaldi, J., Qiu, G. (2019). A Classification-Regression Deep Learning Model for People Counting. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_9
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