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Recognizing Complex Human Activities via Crowd Context

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Wide Area Surveillance

Part of the book series: Augmented Vision and Reality ((Augment Vis Real,volume 6))

Abstract

This chapter examines the problem of classifying collective human activities from video sequences. A collective activity is defined by the existence of the coherent behavior among individuals in a spatial and temporal neighborhood. Examples of collective activities are queuing in a line or talking. Such types of activities cannot be just defined by considering actions of individuals in isolation but rather by observing the interactions of nearby individuals in time and space. In this chapter we discuss recent methods for analyzing collective activities through the concept of crowd context. We present various solutions for modeling the crowd context and demonstrate the flexibility and scalability of the proposed framework in a number of experiments on publicly available datasets of collective human activities.

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References

  1. Collective Activity Dataset. http://www.eecs.umich.edu/vision/activity-dataset.html

  2. Amer, M.R., Todorovic, S.: A chains model for localizing participants of group activities in videos. In: Proceedings of International Conference on Computer Vision (ICCV) (2011)

    Google Scholar 

  3. Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: CVPR (2008)

    Google Scholar 

  4. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. PAMI 24(4), 509–522 (2002)

    Google Scholar 

  5. Bishop, C.M: Pattern Recognition and Machine Learning. Springer, Berlin (2006)

    MATH  Google Scholar 

  6. Breiman, L., Cutler, A.: Random Forest. [online], marzec (2004)

    Google Scholar 

  7. Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.V.: Robust tracking-by-detection using a detector confidence particle filter. In: ICCV (2009)

    Google Scholar 

  8. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm (2001)

  9. Choi, W., Pantofaru, C., Savarese, S.: Detecting and tracking people using an rgb-d camera via multiple detector fusion. In: Challenges and Opportunities in Robot Perception, ICCV, Nov 2011

    Google Scholar 

  10. Choi, W., Savarese, S.: Multiple target tracking in world coordinate with single, minimally calibrated camera. In: ECCV, Sept 2010

    Google Scholar 

  11. Choi, W., Shahid, K., Savarese, S.: What are they doing?: collective activity classification using spatio-temporal relationship among people. In: VSWS (2009)

    Google Scholar 

  12. Choi, W., Shahid, K., Savarese, S.: Learning context for collective activity recognition. In: CVPR (2011)

    Google Scholar 

  13. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)

    Google Scholar 

  14. Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: VS-PETS (2005)

    Google Scholar 

  15. Ess, A., Leibe, B., Schindler, K., van Gool, L.: A mobile vision system for robust multi-person tracking. In: CVPR (2008)

    Google Scholar 

  16. Ess, A., Leibe, B., Schindler, K., van Gool, L.: Robust multi-person tracking from a mobile platform. PAMI 31(10), 1831–1846 (2009)

    Google Scholar 

  17. Fanti, C., Zelnik-Manor, L., Perona, P.: Hybrid models for human motion recognition. In: CVPR, vol. 1, pp. 1166–1173, June 2005

    Google Scholar 

  18. Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. PAMI 32(9), 1627–1645 (2010)

    Google Scholar 

  19. Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. Trans. Pattern Anal. Mach. Intell. 29(12), 2247–2253 (2007)

    Article  Google Scholar 

  20. Hakeem, A., Shah, M.: Learning, detection and representation of multi-agent events in videos. AI 171, 586–605 (2007)

    Google Scholar 

  21. Intille, S., Bobick, A.: Recognizing planned, multiperson action. CVIU 81, 414–445 (2001)

    Google Scholar 

  22. Khan, Z., Balch, T., Dellaert, F.: MCMC-based particle filtering for tracking a variable number of interacting targets. PAMI 27, 1805–1819 (2005)

    Google Scholar 

  23. Kim, T., Wong, S.-f., Cipolla, R.: Tensor canonical correlation analysis for action classification. In: CVPR, June 2007

    Google Scholar 

  24. Kohli, P., Torr, P.H.S.: Dynamic graph cuts and their applications in computer vision In: Computer Vision: Detection, Recognition and Reconstruction, pp. 51–108 (2010)

    Google Scholar 

  25. Lan, T., Wang, Y., Mori, G., Robinovitch, S.: Retrieving actions in group contexts. In: International Workshop on Sign Gesture Activity (2010)

    Google Scholar 

  26. Lan, T., Wang, Y., Yang, W., Mori, G.: Beyond actions: discriminative models for contextual group activities. In: NIPS (2010)

    Google Scholar 

  27. Laptev, I., Lindeberg, T.: Space-time interest points. In: ICCV (2003)

    Google Scholar 

  28. Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR (2008)

    Google Scholar 

  29. Leibe, B., Leonardis, A., Schiele, B.: Combined object categorization and segmentation with an implicit shape model. In: Statistical Learning in Computer Vision, ECCV (2004)

    Google Scholar 

  30. Li, R., Chellappa, R., Zhou, S.K.: Learning multi-modal densities on discriminative temporal interaction manifold for group activity recognition. In: CVPR (2009)

    Google Scholar 

  31. Liu, J., Ali, S., Shah, M.: Recognizing human actions using multiple features. In: CVPR (2008)

    Google Scholar 

  32. Liu, J., Kuipers, B., Savarese, S.: Recognizing human actions by attributes. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (2011)

    Google Scholar 

  33. Liu, J., Luo, J., Shah, M.: Recongizing realistic actions from videos “in the wild”. In: CVPR (2009)

    Google Scholar 

  34. Liu, J., Shah, M., Kuipers, B., Savarese, S.: Cross-view action recognition via view knowledge transfer. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (2011)

    Google Scholar 

  35. Lu, W.-L., Little, J.J.: Simultaneous tracking and action recognition using the pca-hog descriptor. In: Proceedings of the 3rd Canadian Conference on Computer and Robot Vision (2006)

    Google Scholar 

  36. Lv, F., Nevatia, R.: Single view human action recognition using key pose matching and viterbi path searching. In: CVPR (2007)

    Google Scholar 

  37. Marszalek, M., Laptev, I., Schmid, C.: Actions in context. In: CVPR (2009)

    Google Scholar 

  38. Niebles, J.C., Chen, C.-W., Fei-Fei, L.: Modeling temporal structure of decomposable motion segments for activity classification. In: Proceedings of the 12th European Conference of Computer Vision (ECCV), Crete, Greece, Sept 2010

    Google Scholar 

  39. Niebles, J.C., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories using spatial-temporal words. IJCV 79, 299–318 (2008)

    Google Scholar 

  40. Patron, A., Marszalek, M., Zisserman, A., Reid, I.: High five: Recognising human interactions in tv shows. In: Proceedings of the BMVC (2010)

    Google Scholar 

  41. Pirsiavash, H., Ramanan, D., Fowlkes, C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: CVPR (2011)

    Google Scholar 

  42. Ramin Mehran, A.O., Shah, M.: Abnormal crowd behavior detection using social force model. In: CVPR (2009)

    Google Scholar 

  43. Ryoo, M.S., Aggarwal, J.K.: Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities. In: ICCV (2009)

    Google Scholar 

  44. Ryoo, M.S., Aggarwal, J.K.: Stochastic representation and recognition of high-level group activities. IJCV 93(2), 183–200 (2010)

    Google Scholar 

  45. Savarese, S., DelPozo, A., Niebles, J., Fei-Fei, L.: Spatial-temporal correlatons for unsupervised action classification. In: WMVC (2008)

    Google Scholar 

  46. Song, Y., Goncalves, L., Perona, P.: Unsupervised learning of human motion. PAMI 25(25), 1–14 (2003)

    Google Scholar 

  47. Swears, E., Hoogs, A.: Learning and recognizing complex multi-agent activities with applications to american football plays. In: WACV (2011)

    Google Scholar 

  48. Turaga, P., Chellappa, R., Subrahmanian, V.S., Udrea, O.: Machine recognition of human activities: a survey. IEEE Trans. Circuits Syst. Video Technol. 18(11), 1473–1488 (2008)

    Google Scholar 

  49. Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: ICCV (2003)

    Google Scholar 

  50. Wojek, C., Walk, S., Roth, S., Schiele, B.: Monocular 3d scene understanding with explicit occlusion reasoning. In: CVPR (2011)

    Google Scholar 

  51. Wojek, C., Walk, S., Schiele, B.: Multi-cue onboard pedestrian detection. In: CVPR (2009)

    Google Scholar 

  52. Wong, S.-F., Kim, T.-K., Cipolla, R.: Learning motion categories using both semantic and structural information. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2007)

    Google Scholar 

  53. Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. IJCV 75(2), 247 (2007)

    Google Scholar 

  54. Yao, A., Gall, J., Van Gool, L.: A hough transform-based voting framework for action recognition. In: CVPR (2010)

    Google Scholar 

  55. Yu, T., Kim, T.-K., Cipolla, R.: Real-time action recognition by spatiotemporal semantic and structural forest. In: Proceedings of British Machine Vision Conference (BMVC) (2010)

    Google Scholar 

  56. Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: CVPR (2008)

    Google Scholar 

  57. Zhou, B., Wang, X., Tang, X.: Understanding collective crowd behaviors: learning mixture model of dynamic pedestrian-agents. In: CVPR (2012)

    Google Scholar 

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Authors and Affiliations

  1. Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, USA

    Wongun Choi & Silvio Savarese

Authors
  1. Wongun Choi
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  2. Silvio Savarese
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Corresponding author

Correspondence to Wongun Choi .

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Editors and Affiliations

  1. Electrical and Computer Engineering, University of Dayton, Ohio, USA

    Vijayan K. Asari

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© 2014 Springer-Verlag Berlin Heidelberg

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Choi, W., Savarese, S. (2014). Recognizing Complex Human Activities via Crowd Context. In: Asari, V. (eds) Wide Area Surveillance. Augmented Vision and Reality, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/8612_2012_4

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  • DOI: https://doi.org/10.1007/8612_2012_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37840-9

  • Online ISBN: 978-3-642-37841-6

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