Boosting Clusters of Samples for Sequence Matching in Camera Networks

This study introduces a novel classification algorithm for learning and matching sequences in view independent object tracking. The proposed learning method uses adaptive boosting and classification trees on a wide collection (shape, pose, color, texture, etc.) of image features that constitute a mo...

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Bibliographic Details
Published in2010 20th International Conference on Pattern Recognition pp. 400 - 403
Main Authors Takala, Valtteri, Yinghao Cai, Pietikäinen, Matti
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2010
Subjects
Boosting
camera networks
Cameras
Clustering algorithms
Feature extraction
Histograms
Image color analysis
recognition
sequence matching
Tracking
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Summary:This study introduces a novel classification algorithm for learning and matching sequences in view independent object tracking. The proposed learning method uses adaptive boosting and classification trees on a wide collection (shape, pose, color, texture, etc.) of image features that constitute a model for tracked objects. The temporal dimension is taken into account by using k-mean clusters of sequence samples. Most of the utilized object descriptors have a temporal quality also. We argue that with a proper boosting approach and decent number of reasonably descriptive image features it is feasible to do view-independent sequence matching in sparse camera networks. The experiments on real-life surveillance data support this statement.
ISBN:1424475422
9781424475421
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2010.106