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A Data Association Model for Analysis of Crowd Structure

International Journal of Applied Mathematics and Computer Science
Volume 32 (2022): Issue 1 (March 2022)
By:
Open Access
|Mar 2022

References

  1. Bazzani, L., Cristani, M. and Murino, V. (2012). Decentralized particle filter for joint individual-group tracking, IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, pp. 1886–1893, DOI: 10.1109/CVPR.2012.6247888.10.1109/CVPR.2012.6247888
    Search in Google ScholarBack to article
  2. Benfold, B. and Reid, I. (2011). Stable multi-target tracking in real-time surveillance video, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, USA, pp. 3457–3464, DOI: 10.1109/CVPR.2011.5995667.10.1109/CVPR.2011.5995667
    Search in Google ScholarBack to article
  3. Berclaz, J., Fleuret, F., Turetken, E. and Fua, P. (2011). Multiple object tracking using k-shortest paths optimization, IEEE Transactions on Pattern Analysis and Machine Intelligence 33(9): 1806–1819, DOI: 10.1109/TPAMI.2011.21.10.1109/TPAMI.2011.2121282851
    Search in Google ScholarBack to article
  4. Bochinski, E., Senst, T. and Sikora, T. (2018). Extending IOU based multi-object tracking by visual information, 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland, New Zealand, pp. 1–6, DOI: 10.1109/AVSS.2018.8639144.10.1109/AVSS.2018.8639144
    Search in Google ScholarBack to article
  5. Ciaparrone, G., Luque Sanchez, F., Tabik, S., Troiano, L., Tagliaferri, R. and Herrera, F. (2020). Deep learning in video multi-object tracking: A survey, Neurocomputing 381: 61–88, DOI: 10.1016/j.neucom.2019.11.023.10.1016/j.neucom.2019.11.023
    Search in Google ScholarBack to article
  6. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection, IEEE Conference on Computer Vision and Pattern Recognition, San Diego, USA, Vol. 1, pp. 886–893, DOI: 10.1109/CVPR.2005.177.10.1109/CVPR.2005.177
    Search in Google ScholarBack to article
  7. Dehghan, A., Modiri Assari, S. and Shah, M. (2015). GMMCP tracker: Globally optimal generalized maximum multi clique problem for multiple object tracking, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4091–4099, DOI: 10.1109/CVPR.2015.7299036.10.1109/CVPR.2015.7299036
    Search in Google ScholarBack to article
  8. Dollár, P., Appel, R., Belongie, S. and Perona, P. (2014). Fast feature pyramids for object detection, IEEE Transactions on Pattern Analysis and Machine Intelligence 36(8): 1532–1545, DOI: 10.1109/TPAMI.2014.2300479.10.1109/TPAMI.2014.230047926353336
    Search in Google ScholarBack to article
  9. Edman, V., Andersson, M., Granström, K. and Gustafsson, F. (2013). Pedestrian group tracking using the GM-PHD filter, European Signal Processing Conference (EUSIPCO), Marrakech, Morocco, pp. 1–5.
    Search in Google ScholarBack to article
  10. Ferryman, J. and Shahrokni, A. (2009). PETS2009: Dataset and challenge, 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, Snowbird, USA, DOI: 10.1109/PETS-WINTER.2009.5399556.10.1109/PETS-WINTER.2009.5399556
    Search in Google ScholarBack to article
  11. Garcia-Martin, A., Sanchez-Matilla, R. and Martinez, J.M. (2017). Hierarchical detection of persons in groups, Signal, Image and Video Processing 11(7): 1181–1188, DOI: 10.1007/s11760-017-1073-z.10.1007/s11760-017-1073-z
    Search in Google ScholarBack to article
  12. Ge, W., Collins, R.T. and Ruback, R.B. (2012). Vision-based analysis of small groups in pedestrian crowds, IEEE Transactions on Pattern Analysis and Machine Intelligence 34(5): 1003–1016, DOI: 10.1109/TPAMI.2011.176.10.1109/TPAMI.2011.17621844622
    Search in Google ScholarBack to article
  13. Gong, S., Han, H., Shan, S. and Chen, X. (2016). Actions recognition in crowd based on coarse-to-fine multi-object tracking, Asian Conference on Computer Vision, Taipei, Taiwan, pp. 478–490, DOI: 10.1007/978-3-319-54526-4_35.10.1007/978-3-319-54526-4_35
    Search in Google ScholarBack to article
  14. Heili, A. and Odobez, J.-M. (2013). Parameter estimation and contextual adaptation for a multi-object tracking CRF model, IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), Clearwater, USA, pp. 14–21, DOI: 10.1109/PETS.2013.6523790.10.1109/PETS.2013.6523790
    Search in Google ScholarBack to article
  15. Hofmann, M., Haag, M. and Rigoll, G. (2013). Unified hierarchical multi-object tracking using global data association, IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), Clearwater, USA, pp. 22–28, DOI: 10.1109/PETS.2013.6523791.10.1109/PETS.2013.6523791
    Search in Google ScholarBack to article
  16. Jacques, J.C.S., Braun, A., Soldera, J., Musse, S.R. and Jung, C.R. (2007). Understanding people motion in video sequences using Voronoi diagrams, Pattern Analysis and Applications 10(4): 321–332, DOI: 10.1007/s10044-007-0070-1.10.1007/s10044-007-0070-1
    Search in Google ScholarBack to article
  17. Kasprzak, W., Wilkowski, A. and Czapnik, K. (2012). Hand gesture recognition based on free-form contours and probabilistic inference, International Journal of Applied Mathematics and Computer Science 22(2): 437–448, DOI: 10.2478/v10006-012-0033-6.10.2478/v10006-012-0033-6
    Search in Google ScholarBack to article
  18. Li, D., Zhu, J., Xu, B., Lu, M. and Li, M. (2018). An ant-based filtering random-finite-set approach to simultaneous localization and mapping, International Journal of Applied Mathematics and Computer Science 28(3): 505–519, DOI: 10.2478/amcs-2018-0039.10.2478/amcs-2018-0039
    Search in Google ScholarBack to article
  19. Mazzon, R., Poiesi, F. and Cavallaro, A. (2013). Detection and tracking of groups in crowd, IEEE International Conference on Advanced Video and Signal Based Surveil-lance (AVSS), Krakow, Poland, pp. 202–207, DOI: 10.1109/AVSS.2013.6636640.10.1109/AVSS.2013.6636640
    Search in Google ScholarBack to article
  20. Milan, A., Roth, S. and Schindler, K. (2014). Continuous energy minimization for multitarget tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence 36(1): 58–72, DOI: 10.1109/TPAMI.2013.103.10.1109/TPAMI.2013.10324231866
    Search in Google ScholarBack to article
  21. Park, M.-W. and Brilakis, I. (2016). Continuous localization of construction workers via integration of detection and tracking, Automation in Construction 72(Part 2): 129–142, DOI: 10.1016/j.autcon.2016.08.039.10.1016/j.autcon.2016.08.039
    Search in Google ScholarBack to article
  22. Raj, K.S. and Poovendran, R. (2014). Pedestrian detection and tracking through hierarchical clustering, International Conference on Information Communication and Embedded Systems, Chennai, India, pp. 1–4, DOI: 10.1109/ICICES.2014.7033991.10.1109/ICICES.2014.7033991
    Search in Google ScholarBack to article
  23. Ren, W., Kang, D., Tang, Y. and Chan, A.B. (2018). Fusing crowd density maps and visual object trackers for people tracking in crowd scenes, IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, pp. 5353–5362, DOI: 10.1109/CVPR.2018.00561.10.1109/CVPR.2018.00561
    Search in Google ScholarBack to article
  24. Rezatofighi, S.H., Milan, A., Zhang, Z., Shi, Q., Dick, A. and Reid, I. (2015). Joint probabilistic data association revisited, IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, pp. 3047–3055, DOI: 10.1109/ICCV.2015.349.10.1109/ICCV.2015.349
    Search in Google ScholarBack to article
  25. Rodriguez, M., Sivic, J., Laptev, I. and Audibert, J.-Y. (2011). Data-driven crowd analysis in videos, 2011 International Conference on Computer Vision, Barcelona, Spain, pp. 1235–1242, DOI: 10.1109/ICCV.2011.6126374.10.1109/ICCV.2011.6126374
    Search in Google ScholarBack to article
  26. Shao, J., Change Loy, C. and Wang, X. (2014). Scene-independent group profiling in crowd, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, pp. 2219–2226, DOI: 10.1109/CVPR.2014.285.10.1109/CVPR.2014.285
    Search in Google ScholarBack to article
  27. Tang, S., Andriluka, M., Milan, A., Schindler, K., Roth, S. and Schiele, B. (2013). Learning people detectors for tracking in crowded scenes, Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia, pp. 1049–1056, DOI: 10.1109/ICCV.2013.134.10.1109/ICCV.2013.134
    Search in Google ScholarBack to article
  28. Wang, Q., Chen, M. and Li, X. (2017). Quantifying and detecting collective motion by manifold learning, AAAI Conference on Artificial Intelligence, San Francisco, USA, pp. 4292–4298, DOI: 10.5555/3298023.3298190.
    Search in Google ScholarBack to article
  29. Wang, Q., Chen, M., Nie, F. and Li, X. (2020a). Detecting coherent groups in crowd scenes by multiview clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence 42(1): 46–58, DOI: 10.1109/TPAMI.2018.2875002.10.1109/TPAMI.2018.287500230307858
    Search in Google ScholarBack to article
  30. Wang, Q., Gao, J., Lin, W. and Li, X. (2020b). NWPU-crowd: A large-scale benchmark for crowd counting and localization, IEEE Transactions on Pattern Analysis and Machine Intelligence 43(6): 2141–2149, DOI: 10.1109/TPAMI.2020.3013269.10.1109/TPAMI.2020.301326932750840
    Search in Google ScholarBack to article
  31. Wang, Q., Gao, J., Lin, W. and Yuan, Y. (2020c). Pixel-wise crowd understanding via synthetic data, International Journal of Computer Vision 129(1): 225–245, DOI: 10.1007/s11263-020-01365-4.10.1007/s11263-020-01365-4
    Search in Google ScholarBack to article
  32. Wen, L., Lei, Z., Lyu, S., Li, S. Z. and Yang, M.-H. (2016). Exploiting hierarchical dense structures on hypergraphs for multi-object tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence 38(10): 1983–1996, DOI: 10.1109/TPAMI.2015.2509979.10.1109/TPAMI.2015.250997926700969
    Search in Google ScholarBack to article
  33. Yang, B. and Nevatia, R. (2014). Multi-target tracking by online learning a CRF model of appearance and motion patterns, International Journal of Computer Vision 107(2): 203–217, DOI: 10.1007/s11263-013-0666-4.10.1007/s11263-013-0666-4
    Search in Google ScholarBack to article
  34. Yu, H., Zhou, Y., Simmons, J., Przybyla, C.P., Lin, Y., Fan, X., Mi, Y. and Wang, S. (2016). Groupwise tracking of crowded similar-appearance targets from low-continuity image sequences, IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 952–960, DOI: 10.1109/CVPR.2016.109.10.1109/CVPR.2016.109
    Search in Google ScholarBack to article
  35. Zhang, L., He, Z., Gu, M. and Yu, H. (2018). Crowd segmentation method based on trajectory tracking and prior knowledge learning, Arabian Journal for Science and Engineering 43(12): 7143–7152, DOI: 10.1007/s13369-017-2995-z.10.1007/s13369-017-2995-z
    Search in Google ScholarBack to article
  36. Zhang, S., Wang, J., Wang, Z., Gong, Y. and Liu, Y. (2015). Multi-target tracking by learning local-to-global trajectory models, Pattern Recognition 48(2): 580–590, DOI: 10.1016/j.patcog.2014.08.013.10.1016/j.patcog.2014.08.013
    Search in Google ScholarBack to article
  37. Zhou, X., Zhuo, J. and Krahenbuhl, P. (2019). Bottom-up object detection by grouping extreme and center points, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, pp. 850–859, DOI: 10.1109/CVPR.2019.00094.10.1109/CVPR.2019.00094
    Search in Google ScholarBack to article
  38. Zhu, F., Wang, X. and Yu, N. (2014). Crowd tracking with dynamic evolution of group structures, European Conference on Computer Vision, Zurich, Switzerland, pp. 139–154, DOI: 10.1007/978-3-319-10599-4_10.10.1007/978-3-319-10599-4_10
    Search in Google ScholarBack to article
  39. Zhu, F., Wang, X. and Yu, N. (2018). Crowd tracking by group structure evolution, IEEE Transactions on Circuits and Systems for Video Technology 28(3): 772–786, DOI: 10.1109/TCSVT.2016.2615460.10.1109/TCSVT.2016.2615460
    Search in Google ScholarBack to article
  40. Zitouni, M.S., Bhaskar, H. and Al-Mualla, M.E. (2016). Robust background modeling and foreground detection using dynamic textures, International Conference on Computer Vision Theory and Applications (VISIGRAPP’16), Rome, Italy, pp. 403–410, DOI: 10.5220/0005724204030410.10.5220/0005724204030410
    Search in Google ScholarBack to article
  41. Zitouni, M.S., Sluzek, A. and Bhaskar, H. (2019a). CNN-based analysis of crowd structure using automatically annotated training data, IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Taipei, Taiwan, pp. 1–8, DOI: 10.1109/AVSS.2019.8909846.10.1109/AVSS.2019.8909846
    Search in Google ScholarBack to article
  42. Zitouni, M.S., Sluzek, A. and Bhaskar, H. (2019b). Visual analysis of socio-cognitive crowd behaviors for surveillance: A survey and categorization of trends and methods, Engineering Applications of Artificial Intelligence 82: 294–312, DOI: 10.1016/j.engappai.2019.04.012.10.1016/j.engappai.2019.04.012
    Search in Google ScholarBack to article
  43. Zitouni, M.S., Sluzek, A. and Bhaskar, H. (2020). Towards understanding socio-cognitive behaviors of crowds from visual surveillance data, Multimedia Tools and Applications 79(3): 1781–1799, DOI: 10.1007/s11042-019-08201-z.10.1007/s11042-019-08201-z
    Search in Google ScholarBack to article
DOI: https://doi.org/10.34768/amcs-2022-0007 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
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Language: English
Page range: 81 - 94
Submitted on: May 16, 2021
Accepted on: Oct 19, 2021
Published on: Mar 31, 2022
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Keywords:
data association,
visual surveillance,
crowd analysis,
algebraic model
Related subjects:
Mathematics,
Applied mathematics

© 2022 M. Sami Zitouni, Andrzej Śluzek, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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