Counting Pedestrians in Bidirectional Scenarios Using Zenithal Depth Images

  • Pablo Vera
  • Daniel Zenteno
  • Joaquín Salas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7914)


In this document, we describe a people counting system that can precisely detect people as they are seen from a zenithal depth camera pointing at the floor. In particular, we are interested in scenarios where there are two preferred directions of motion. In our framework, we detect people using a Support Vector Machine classifier, follow their trajectory by modeling the problem of matching observations between frames as a bipartite graph, and determine the direction of their motion with a bi-directional classifier. We include experimental evidence, from four different scenarios, for each major stage of our method.


Support Vector Machine Bipartite Graph Depth Image Driver Assistance System Oriented Gradient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pablo Vera
    • 1
  • Daniel Zenteno
    • 1
  • Joaquín Salas
    • 1
  1. 1.Instituto Politécnico NacionalColinas del CimatarioMéxico

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