Convoy Detection in Crowded Surveillance Videos

  • Zeyd BoukhersEmail author
  • Yicong Wang
  • Kimiaki Shirahama
  • Kuniaki Uehara
  • Marcin Grzegorzek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9997)


This paper proposes detection of convoys in a crowded surveillance video. A convoy is defined as a group of pedestrians who are moving or standing together for a certain period of time. To detect such convoys, we firstly address pedestrian detection in a crowded scene, where small regions of pedestrians and their strong occlusions render usual object detection methods ineffective. Thus, we develop a method that detects pedestrian regions by clustering feature points based on their spatial characteristics. Then, positional transitions of pedestrian regions are analysed by our convoy detection method that consists of the clustering and intersection processes. The former finds groups of pedestrians in one frame by flexibly handling their relative spatial positions, and the latter refines groups into convoys by considering their temporal consistences over multiple frames. The experimental results on a challenging dataset shows the effectiveness of our convoy detection method.


Crowded video surveillance Group activities Convoy detection Pedestrian detection in crowded scenes 



The research work by Zeyd Boukhers leading to this article has been funded by the German Academic Exchange Service (DAAD). Research and development activities in this article have been in part supported by the German Federal Ministry of Education and Research within the project “Cognitive Village: Adaptively Learning Technical Support System for Elderly” (Grant Number: 16SV7223K).


  1. 1.
    Amer, M.R., Todorovic, S.: A chains model for localizing participants of group activities in videos. In: Proceedings of ICCV 2011, pp. 786–793 (2011)Google Scholar
  2. 2.
    Chang, M.C., Krahnstoever, N., Ge, W.: Probabilistic group-level motion analysis and scenario recognition. In: Proceedings of ICCV 2011, pp. 747–754 (2011)Google Scholar
  3. 3.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of KDD 1996, pp. 226–231 (1996)Google Scholar
  4. 4.
    Ge, W., Collins, R.T., Ruback, R.B.: Vision-based analysis of small groups in pedestrian crowds. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 1003–1016 (2012)CrossRefGoogle Scholar
  5. 5.
    Jeung, H., Shen, H.T., Zhou, X.: Convoy queries in spatio-temporal databases. In: Proceedings of ICDE 2008, pp. 1457–1459 (2008)Google Scholar
  6. 6.
    Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S., Shen, H.T.: Discovery of convoys in trajectory databases. Proc. VLDB Endowment 1(1), 1068–1080 (2008)CrossRefGoogle Scholar
  7. 7.
    Lan, T., Wang, Y., Yang, W., Robinovitch, S.N., Mori, G.: Discriminative latent models for recognizing contextual group activities. IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1549–1562 (2012)CrossRefGoogle Scholar
  8. 8.
    Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: Proceedings of CVPR 2010, pp. 1975–1981 (2010)Google Scholar
  9. 9.
    Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: Proceedings of CVPR 2009, pp. 935–942 (2009)Google Scholar
  10. 10.
    Moussaid, M., Perozo, N., Garnier, S., Helbing, D., Theraulaz, G.: The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PLoS ONE 5(4), 1–7 (2010)CrossRefGoogle Scholar
  11. 11.
    Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006). doi: 10.1007/11744023_34 CrossRefGoogle Scholar
  12. 12.
    Shao, J., Kang, K., Loy, C.C., Wang, X.: Deeply learned attributes for crowded scene understanding. In: Proceedings of CVPR 2015, pp. 4657–4666 (2015)Google Scholar
  13. 13.
    Shao, J., Loy, C.C., Wang, X.: Scene-independent group profiling in crowd. In: Proceedings of CVPR 2014, pp. 2227–2234 (2014)Google Scholar
  14. 14.
    Sinha, S.N., Frahm, J.-M., Pollefeys, M., Genc, Y.: Feature tracking and matching in video using programmable graphics hardware. Mach. Vis. Appl. 22(1), 207–217 (2011)CrossRefGoogle Scholar
  15. 15.
    Solmaz, B., Moore, B.E., Shah, M.: Identifying behaviors in crowd scenes using stability analysis for dynamical systems. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 2064–2070 (2012)CrossRefGoogle Scholar
  16. 16.
    Vedaldi, A., Fulkerson, B.: VLFeat: an open and portable library of computer vision algorithms (2008). Accessed 21 Apr 2016
  17. 17.
    Wang, X., Ma, X., Grimson, W.E.L.: Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 539–555 (2009)CrossRefGoogle Scholar
  18. 18.
    Yi, S., Li, H., Wang, X.: Pedestrian travel time estimation in crowded scenes. In: Proceedings of ICCV 2015, pp. 3137–3145 (2015)Google Scholar
  19. 19.
    Yi, S., Li, H., Wang, X.: Understanding pedestrian behaviors from stationary crowd groups. In: Proceedings of CVPR 2015, pp. 3488–3496 (2015)Google Scholar
  20. 20.
    Yi, S., Wang, X., Lu, C., Jia, J.: L0 regularized stationary time estimation for crowd group analysis. In: Proceedings of CVPR 2014, pp. 2219–2226 (2014)Google Scholar
  21. 21.
    Zhou, B., Wang, X., Tang, X.: Understanding collective crowd behaviors: learning a mixture model of dynamic pedestrian-agents. In: Proceedings of CVPR 2012, pp. 2871–2878 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Zeyd Boukhers
    • 1
    Email author
  • Yicong Wang
    • 2
  • Kimiaki Shirahama
    • 1
  • Kuniaki Uehara
    • 2
  • Marcin Grzegorzek
    • 1
  1. 1.Research Group for Pattern RecognitionUniversity of SiegenSiegenGermany
  2. 2.Graduate School of System InformaticsKobe UniversityKobeJapan

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