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A Hierarchical System for Recognition, Tracking and Pose Estimation

P. Zehnder, E. Koller-Meier and L. Van Gool
Machine Learning for Multimodal Interaction: First International Workshop, MLMI 2004, Martigny, Switzerland, June 21-23, 2004, Revised Selected Papers
Martigny, Switzerland, January 2005

Abstract

This paper presents a new system for recognition, tracking and pose estimation of people in video sequences. It is based on the wavelet transform from the upper body part and uses Support Vector Machines (SVM) for classification. Recognition is carried out hierarchi- cally by first recognizing people and then individual characters. The char- acteristic features that best discriminate one person from another are learned automatically. Tracking is solved via a particle filter that utilizes the SVM output and a first order kinematic model to obtain a robust scheme that successfully handles occlusion, different poses and camera zooms. For pose estimation a collection of SVM classifiers is evaluated to detect specific, learned poses.


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@InProceedings{eth_biwi_00313,
  author = {P. Zehnder and E. Koller-Meier and L. Van Gool},
  title = {A Hierarchical System for Recognition, Tracking and Pose Estimation},
  booktitle = {Machine Learning for Multimodal Interaction: First International Workshop, MLMI 2004, Martigny, Switzerland, June 21-23, 2004, Revised Selected Papers},
  year = {2005},
  month = {January},
  pages = {p. 329},
  volume = {3361 / 2005},
  editor = {Samy Bengio and HervĂ© Bourlard},
  series = {Lecture Notes in Computer Science},
  publisher = {Springer-Verlag GmbH},
  keywords = {}
}