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A Hierarchical System for Recognition, Tracking and Pose Estimation

P. Zehnder, E. Koller-Meier, R. Fransens, and L. Van Gool
Cognitive Vision Systems
, Ed.
Springer, 2005

Abstract

This chapter presents a system for the recognition, tracking and pose estimation of people in video sequences. It is based on a careful selection of Haar wavelet features and uses Support Vector Machines (SVM) in spaces of reduced dimensionality for classification. Recognition is carried out hierarchically by using a set of detectors and discriminators for people and poses. The characteristic fea- tures used in the individual nodes 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.


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@InCollection{eth_biwi_00326,
  title = {A Hierarchical System for Recognition, Tracking and Pose Estimation},
  booktitle = {Cognitive Vision Systems},
  pages = {329-340},
  year = {2005},
  publisher = {Springer},
  keywords = {detection, tracking, recognition, wavelet transform, svm, particle filter}
}