Title

Online Detection And Classification Of Moving Objects Using Progressively Improving Detectors

Abstract

Boosting based detection methods have successfully been used for robust detection of faces and pedestrians. However, a very large amount of labeled examples are required for training such a classifier. Moreover, once trained, the boosted classifier cannot adjust to the particular scenario in which it is employed. In this paper, we propose a co-training based approach to continuously label incoming data and use it for online update of the boosted classifier that was initially trained from a small labeled example set. The main contribution of our approach is that it is an online procedure in which separate views (features) of the data are used for co-training, while the combined view (all features) is used to make classification decisions in a single boosted framework. The features used for classification are derived from Principal Component Analysis of the appearance templates of the training examples. In order to speed up the classification, background modeling is used to prune away stationary regions in an image. Our experiments indicate that starting from a classifier trained on a small training set, significant performance gains can be made through online updation from the unlabeled data. © 2005 IEEE.

Publication Date

1-1-2005

Publication Title

Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005

Volume

I

Number of Pages

696-701

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CVPR.2005.259

Socpus ID

33745173515 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33745173515

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