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


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%.


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


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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

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