People Orientation Recognition by Mixtures of Wrapped Distributions on Random Trees

  • Davide Baltieri
  • Roberto Vezzani
  • Rita Cucchiara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7576)


The recognition of people orientation in single images is still an open issue in several real cases, when the image resolution is poor, body parts cannot be distinguished and localized or motion cannot be exploited. However, the estimation of a person orientation, even an approximated one, could be very useful to improve people tracking and re-identification systems, or to provide a coarse alignment of body models on the input images. In these situations, holistic features seem to be more effective and faster than model based 3D reconstructions. In this paper we propose to describe the people appearance with multi-level HoG feature sets and to classify their orientation using an array of Extremely Randomized Trees classifiers trained on quantized directions. The outputs of the classifiers are then integrated into a global continuous probability density function using a Mixture of Approximated Wrapped Gaussian distributions. Experiments on the TUD Multiview Pedestrians, the Sarc3D, and the 3DPeS datasets confirm the efficacy of the method and the improvement with respect to state of the art approaches.


Orientation recognition Mixtures of Wrapped Distributions Random Trees 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Davide Baltieri
    • 1
  • Roberto Vezzani
    • 1
  • Rita Cucchiara
    • 1
  1. 1.DIEFUniversity of Modena and Reggio EmiliaModenaItaly

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