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An adaptive appearance model approach for model-based articulated object tracking

2006

Conference Paper

ps


The detection and tracking of three-dimensional human body models has progressed rapidly but successful approaches typically rely on accurate foreground silhouettes obtained using background segmentation. There are many practical applications where such information is imprecise. Here we develop a new image likelihood function based on the visual appearance of the subject being tracked. We propose a robust, adaptive, appearance model based on the Wandering-Stable-Lost framework extended to the case of articulated body parts. The method models appearance using a mixture model that includes an adaptive template, frame-to-frame matching and an outlier process. We employ an annealed particle filtering algorithm for inference and take advantage of the 3D body model to predict self occlusion and improve pose estimation accuracy. Quantitative tracking results are presented for a walking sequence with a 180 degree turn, captured with four synchronized and calibrated cameras and containing significant appearance changes and self-occlusion in each view.

Author(s): Balan, A. and Black, M. J.
Book Title: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR
Volume: 1
Pages: 758--765
Year: 2006
Month: June

Department(s): Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Address: New York, NY

Links: pdf

BibTex

@inproceedings{Balan:CVPR:2006,
  title = {An adaptive appearance model approach for model-based articulated object tracking},
  author = {Balan, A. and Black, M. J.},
  booktitle = {Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR},
  volume = {1},
  pages = {758--765},
  address = {New York, NY},
  month = jun,
  year = {2006},
  month_numeric = {6}
}