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Calibration-Free Multi-view Crowd Counting

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13669))

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Abstract

Deep learning based multi-view crowd counting (MVCC) has been proposed to handle scenes with large size, in irregular shape or with severe occlusions. The current MVCC methods require camera calibrations in both training and testing, limiting the real application scenarios of MVCC. To extend and apply MVCC to more practical situations, in this paper we propose calibration-free multi-view crowd counting (CF-MVCC), which obtains the scene-level count directly from the density map predictions for each camera view without needing the camera calibrations in the test. Specifically, the proposed CF-MVCC method first estimates the homography matrix to align each pair of camera-views, and then estimates a matching probability map for each camera-view pair. Based on the matching maps of all camera-view pairs, a weight map for each camera view is predicted, which represents how many cameras can reliably see a given pixel in the camera view. Finally, using the weight maps, the total scene-level count is obtained as a simple weighted sum of the density maps for the camera views. Experiments are conducted on several multi-view counting datasets, and promising performance is achieved compared to calibrated MVCC methods that require camera calibrations as input and use scene-level density maps as supervision.

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References

  1. Agarwal, S., et al.: Building Rome in a day. Commun. ACM 54(10), 105–112 (2011)

    Article  Google Scholar 

  2. Ammar Abbas, S., Zisserman, A.: A geometric approach to obtain a bird’s eye view from an image. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (2019)

    Google Scholar 

  3. Bai, S., He, Z., Qiao, Y., Hu, H., Wu, W., Yan, J.: Adaptive dilated network with self-correction supervision for counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4594–4603 (2020)

    Google Scholar 

  4. Bhardwaj, R., Tummala, G.K., Ramalingam, G., Ramjee, R., Sinha, P.: Autocalib: automatic traffic camera calibration at scale. ACM Trans. Sensor Netw. (TOSN) 14(3–4), 1–27 (2018)

    Google Scholar 

  5. von Borstel, M., Kandemir, M., Schmidt, P., Rao, M.K., Rajamani, K., Hamprecht, F.A.: Gaussian process density counting from weak supervision. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 365–380. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_22

    Chapter  Google Scholar 

  6. Chan, A.B., Vasconcelos, N.: Counting people with low-level features and Bayesian regression. IEEE Trans. Image Process. 21(4), 2160–2177 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  7. DeTone, D., Malisiewicz, T., Rabinovich, A.: Deep image homography estimation. arXiv preprint arXiv:1606.03798 (2016)

  8. Dittrich, F., de Oliveira, L.E., Britto Jr, A.S., Koerich, A.L.: People counting in crowded and outdoor scenes using a hybrid multi-camera approach. arXiv preprint arXiv:1704.00326 (2017)

  9. Ferryman, J., Shahrokni, A.: Pets 2009: dataset and challenge. In: 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, pp. 1–6. IEEE (2009)

    Google Scholar 

  10. Ge, W., Collins, R.T.: Crowd detection with a multiview sampler. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 324–337. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15555-0_24

    Chapter  Google Scholar 

  11. Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 2017–2025 (2015)

    Google Scholar 

  12. Jiang, X., et al.: Attention scaling for crowd counting. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020

    Google Scholar 

  13. Junior, J.C.S.J., Musse, S.R., Jung, C.R.: Crowd analysis using computer vision techniques. IEEE Signal Process. Mag. 27(5), 66–77 (2010)

    Google Scholar 

  14. Kang, D., Chan, A.: Crowd counting by adaptively fusing predictions from an image pyramid. In: BMVC (2018)

    Google Scholar 

  15. Le, H., Liu, F., Zhang, S., Agarwala, A.: Deep homography estimation for dynamic scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7652–7661 (2020)

    Google Scholar 

  16. Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: Advances in Neural Information Processing Systems, pp. 1324–1332 (2010)

    Google Scholar 

  17. 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)

    Google Scholar 

  18. Lian, D., Li, J., Zheng, J., Luo, W., Gao, S.: Density map regression guided detection network for RGB-D crowd counting and localization. In: CVPR, pp. 1821–1830 (2019)

    Google Scholar 

  19. Liu, L., Chen, J., Wu, H., Li, G., Li, C., Lin, L.: Cross-modal collaborative representation learning and a large-scale RGBT benchmark for crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4823–4833, June 2021

    Google Scholar 

  20. Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: CVPR, pp. 5099–5108 (2019)

    Google Scholar 

  21. Liu, X., van de Weijer, J., Bagdanov, A.D.: Exploiting unlabeled data in CNNs by self-supervised learning to rank. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1862–1878 (2019)

    Article  Google Scholar 

  22. Liu, X., Yang, J., Ding, W., Wang, T., Wang, Z., Xiong, J.: Adaptive mixture regression network with local counting map for crowd counting. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12369, pp. 241–257. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58586-0_15

    Chapter  Google Scholar 

  23. Liu, Y., Liu, L., Wang, P., Zhang, P., Lei, Y.: Semi-supervised crowd counting via self-training on surrogate tasks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12360, pp. 242–259. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58555-6_15

    Chapter  Google Scholar 

  24. Lu, E., Xie, W., Zisserman, A.: Class-agnostic counting. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 669–684. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20893-6_42

    Chapter  Google Scholar 

  25. Ma, Z., Wei, X., Hong, X., Gong, Y.: Bayesian loss for crowd count estimation with point supervision, pp. 6141–6150 (2019)

    Google Scholar 

  26. Maddalena, L., Petrosino, A., Russo, F.: People counting by learning their appearance in a multi-view camera environment. Pattern Recogn. Lett. 36, 125–134 (2014)

    Article  Google Scholar 

  27. Mishkin, D., Matas, J., Perdoch, M., Lenc, K.: WXBS: wide baseline stereo generalizations. In: British Machine Vision Conference (2015)

    Google Scholar 

  28. Nguyen, T., Chen, S.W., Shivakumar, S.S., Taylor, C.J., Kumar, V.: Unsupervised deep homography: a fast and robust homography estimation model. IEEE Robot. Autom. Lett. 3(3), 2346–2353 (2018)

    Article  Google Scholar 

  29. Oñoro-Rubio, D., López-Sastre, R.J.: Towards perspective-free object counting with deep learning. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 615–629. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_38

    Chapter  Google Scholar 

  30. Pritchett, P., Zisserman, A.: Wide baseline stereo matching. In: International Conference on Computer Vision (1998)

    Google Scholar 

  31. Ranjan, V., Sharma, U., Nguyen, T., Hoai, M.: Learning to count everything. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3394–3403, June 2021

    Google Scholar 

  32. Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 17–35. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_2

    Chapter  Google Scholar 

  33. Rocco, I., Cimpoi, M., Arandjelović, R., Torii, A., Pajdla, T., Sivic, J.: Neighbourhood consensus networks. arXiv preprint arXiv:1810.10510 (2018)

  34. Ryan, D., Denman, S., Fookes, C., Sridharan, S.: Scene invariant multi camera crowd counting. Pattern Recogn. Lett. 44(8), 98–112 (2014)

    Article  Google Scholar 

  35. Sabzmeydani, P., Mori, G.: Detecting pedestrians by learning shapelet features. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)

    Google Scholar 

  36. Sam, D.B., Sajjan, N.N., Maurya, H., Radhakrishnan, V.B.: Almost unsupervised learning for dense crowd counting. In: Thirty-Third AAAI Conference on Artificial Intelligence, vol. 33(1), pp. 8868–8875 (2019)

    Google Scholar 

  37. Sam, D.B., Surya, S., Babu, R.V.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, p. 6 (2017)

    Google Scholar 

  38. Shi, M., Yang, Z., Xu, C., Chen, Q.: Revisiting perspective information for efficient crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7279–7288 (2019)

    Google Scholar 

  39. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  40. Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid CNNs. In: IEEE International Conference on Computer Vision (ICCV), pp. 1879–1888. IEEE (2017)

    Google Scholar 

  41. 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)

    Article  Google Scholar 

  42. Sindagi, V.A., Yasarla, R., Babu, D.S., Babu, R.V., Patel, V.M.: Learning to count in the crowd from limited labeled data. arXiv preprint arXiv:2007.03195 (2020)

  43. Song, Q., et al.: Rethinking counting and localization in crowds: a purely point-based framework. arXiv preprint arXiv:2107.12746 (2021)

  44. Tang, N., Lin, Y.Y., Weng, M.F., Liao, H.Y.: Cross-camera knowledge transfer for multiview people counting. IEEE Trans. Image Process. 24(1), 80–93 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  45. Wan, J., Liu, Z., Chan, A.B.: A generalized loss function for crowd counting and localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1974–1983, June 2021

    Google Scholar 

  46. Wang, Q., Gao, J., et al.: Learning from synthetic data for crowd counting in the wild. In: CVPR, pp. 8198–8207 (2019)

    Google Scholar 

  47. Yan, Z., et al.: Perspective-guided convolution networks for crowd counting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 952–961 (2019)

    Google Scholar 

  48. Yang, S.D., Su, H.T., Hsu, W.H., Chen, W.C.: Class-agnostic few-shot object counting. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 870–878 (2021)

    Google Scholar 

  49. Yang, Y., Li, G., Wu, Z., Su, L., Huang, Q., Sebe, N.: Reverse perspective network for perspective-aware object counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4374–4383 (2020)

    Google Scholar 

  50. Yang, Y., Li, G., Wu, Z., Su, L., Huang, Q., Sebe, N.: Weakly-supervised crowd counting learns from sorting rather than locations. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 1–17. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58598-3_1

    Chapter  Google Scholar 

  51. 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)

    Google Scholar 

  52. Zhang, J., Wang, C., Liu, S., Jia, L., Ye, N., Wang, J., Zhou, J., Sun, J.: Content-aware unsupervised deep homography estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 653–669. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_38

    Chapter  Google Scholar 

  53. Zhang, Q., Chan, A.B.: Wide-area crowd counting via ground-plane density maps and multi-view fusion CNNs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8297–8306 (2019)

    Google Scholar 

  54. Zhang, Q., Chan, A.B.: 3d crowd counting via multi-view fusion with 3d gaussian kernels. In: AAAI Conference on Artificial Intelligence, pp. 12837–12844 (2020)

    Google Scholar 

  55. Zhang, Q., Lin, W., Chan, A.B.: Cross-view cross-scene multi-view crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 557–567 (2021)

    Google Scholar 

  56. 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)

    Google Scholar 

  57. Zhao, Z., Shi, M., Zhao, X., Li, L.: Active crowd counting with limited supervision. arXiv preprint arXiv:2007.06334 (2020)

  58. Zheng, L., Li, Y., Mu, Y.: Learning factorized cross-view fusion for multi-view crowd counting. In: 2021 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2021)

    Google Scholar 

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Acknowledgements

This work was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (CityU 11212518, CityU 11215820), and by a Strategic Research Grant from City University of Hong Kong (Project No. 7005665).

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Authors and Affiliations

  1. College of Computer Science & Software Engineering, Shenzhen University, Shenzhen, China

    Qi Zhang

  2. Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China

    Qi Zhang & Antoni B. Chan

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  1. Qi Zhang
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  2. Antoni B. Chan
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Correspondence to Qi Zhang .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Zhang, Q., Chan, A.B. (2022). Calibration-Free Multi-view Crowd Counting. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13669. Springer, Cham. https://doi.org/10.1007/978-3-031-20077-9_14

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  • DOI: https://doi.org/10.1007/978-3-031-20077-9_14

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