Advertisement

Machine Vision and Applications

, Volume 23, Issue 2, pp 243–253 | Cite as

Detecting people in dense crowds

  • Chern-Horng Sim
  • Ekambaram Rajmadhan
  • Surendra Ranganath
Original Paper

Abstract

We propose a scheme to detect individuals in any image frame of a video sequence showing densely crowded scenes against cluttered backgrounds. The method uses only spatial information, and in an initial pass through the image a trained Viola–Jones-type local detector is used to locate individuals in the densely crowded scene. This yields a large number of false alarms. Hence, in a second step, we seek to reduce the false alarms, and propose two methods for this. In the first, color information from the initially detected windows is passed to a classifier to reduce the false alarms. This classifier consists of a cascade of boosted classifiers with Haar-like features as input and is trained with color information from local windows. In the second method, a weak perspective model of an uncalibrated camera is used to further reduce the false alarm rate while maintaining the detection rate. This is based on the size and locations of the detections in the image frame, without the use of any 3D world information. Results are presented in the form of receiver operating characteristic curves. For instance, at a 79.0% detection accuracy, the false alarm rate is 20.3%.

Keywords

Video surveillance Dense crowds Head detection Boosted classifiers Weak perspective camera model Outlier removal 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bradski G., Kaehler A., Pisarevsky V.: Learning-based computer vision with Intel’s open source computer vision library. Intel Technol. J. 9(2), 118–131 (2005)Google Scholar
  2. 2.
    Casas J., Sitjes A., Folch P.: Mutual feedback scheme for face detection and tracking aimed at density estimation in demonstrations. Vis. Image Signal Process. 152(3), 334–346 (2005)CrossRefGoogle Scholar
  3. 3.
    Clabian, M., Rtzer, H., Bischof, H., Kropatsch, W.: Head detection and localization from sparse 3D data. In: Proceedings of the 24th DAGM Symposium on Pattern Recognition, pp. 395–402 (2002)Google Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)Google Scholar
  5. 5.
    Dollar, P., Tu, Z., Tao, H., Belongie, S.: Feature mining for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
  6. 6.
    Fuentes L., Velastin S.: People tracking in surveillance applications. Image Vis. Comput. 24(11), 1165–1171 (2006)CrossRefGoogle Scholar
  7. 7.
    Haritaoglu I., Harwood D., Davis L.S.: W4: Real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 809–830 (2000)CrossRefGoogle Scholar
  8. 8.
    Heikkilä J., Silvén O.: A real-time system for monitoring of cyclists and pedestrians. Image Vis. Comput. 22(7), 563–570 (2004)CrossRefGoogle Scholar
  9. 9.
    Hoiem, D., Efros, A., Hebert, M.: Putting objects in perspective. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2137–2144 (2006)Google Scholar
  10. 10.
    Hu W., Tan T., Wang L., Maybank S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern. 34(3), 334–352 (2004)CrossRefGoogle Scholar
  11. 11.
    Jin, Y., Mokhtarian, F.: Towards robust head tracking by particles. In: IEEE International Conference on Image Processing, vol. 3, pp. 864–867 (2005)Google Scholar
  12. 12.
    Joo, H., Jang, B., Suman, S., Rhee, P.: Use of nested k-means for robust head location in visual surveillance system. In: Pacific Rim International Conference on Artificial Intelligence, pp. 583–592 (2006)Google Scholar
  13. 13.
    Kim C., Hwang J.N.: Object-based video abstraction for video surveillance systems. IEEE Trans. Circuits Syst. Video Technol. 12(12), 1128–1138 (2002)CrossRefGoogle Scholar
  14. 14.
    Kim, Y.G., Lee, J.E., Kim, S.J., Choi, S.M., Park, G.T.: Head detection of the car occupant based on contour models and support vector machines. In: Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, pp. 59–61 (2005)Google Scholar
  15. 15.
    Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 878–885 (2005)Google Scholar
  16. 16.
    Li, Y., Ai, H., Huang, C., Lao, S.: Robust head tracking based on a multi-state particle filter. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 335–340 (2006)Google Scholar
  17. 17.
    Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: IEEE International Conference on Image Processing, vol. 1, pp. 900–903 (2002)Google Scholar
  18. 18.
    Meer P., Mintz D., Rosenfeld A., Kim D.: Robust regression methods for computer vision: A review. Int. J. Comput. Vis. 6(1), 59–70 (1991)CrossRefGoogle Scholar
  19. 19.
    Mikolajczyk, K., Schmid, C., Zisserman, A.: Human detection based on a probabilistic assembly of robust part detectors. In: European Conference on Computer Vision, vol. 1, pp. 69–82 (2004)Google Scholar
  20. 20.
    Munder S., Gavrila D.: An experimental study on pedestrian classification. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1863–1868 (2006)CrossRefGoogle Scholar
  21. 21.
    Renno, J., Orwell, J., Jones, G.: Learning surveillance tracking models for the self-calibrated ground plane. In: British Machine Vision Conference (2002)Google Scholar
  22. 22.
    Rittscher, J., Tu, P., Krahnstoever, N.: Simultaneous estimation of segmentation and shape. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 486–493 (2005)Google Scholar
  23. 23.
    Sabzmeydani, P., Mori, G.: Detecting pedestrians by learning shapelet features. In: IEEE Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
  24. 24.
    Seemann, E., Fritz, M., Schiele, B.: Towards robust pedestrian detection in crowded image sequences. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1 (2007)Google Scholar
  25. 25.
    Sim, C.H., Ranganath, S.: Reducing false alarms for detections in crowd. In: Asian Conference on Computer Vision Workshop on Multi-dimensional and Multi-view Image Processing, pp. 164–169 (2007)Google Scholar
  26. 26.
    Tuzel, O., Porikli, F., Meer, P.: Human detection via classification on riemannian manifolds. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1 (2007)Google Scholar
  27. 27.
    Valera M., Velastin S.A.: Intelligent distributed surveillance systems: a review. IEE Proc. Vis. Image Signal Process. 152(2), 192–204 (2005)CrossRefGoogle Scholar
  28. 28.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)Google Scholar
  29. 29.
    Viola P., Jones M.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  30. 30.
    Viola P., Jones M., Snow D.: Detecting pedestrians using patterns of motion and appearance. Int. J. Comput. Vis. 63(2), 153–161 (2005)CrossRefGoogle Scholar
  31. 31.
    Wisnowski J., Montgomery D., Simpson J.: A comparative analysis of multiple outlier detection procedures in the linear regression model. Comput. Stat. Data Anal. 36(3), 351–382 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  32. 32.
    Wu B., Nevatia R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. Int. J. Comput. Vis. 75(2), 247–266 (2007)CrossRefGoogle Scholar
  33. 33.
    Zhu, Q., Yeh, M., Cheng, K., Avidan, S.: Fast human detection using a cascade of histograms of oriented gradients. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1491–1498 (2006)Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Chern-Horng Sim
    • 1
  • Ekambaram Rajmadhan
    • 2
  • Surendra Ranganath
    • 3
  1. 1.Singapore Technologies Dynamics Pte LtdSingaporeSingapore
  2. 2.Department of Computer Science and EngineeringUniversity of South FloridaTampaUSA
  3. 3.Indian Institute of Technology GandhinagarChandkheda, AhmedabadIndia

Personalised recommendations