Object Classification in Visual Surveillance Using Adaboost

Renno, John-Paul; Makris, Dimitrios; Jones, Graeme A.

doi:10.1109/CVPR.2007.383514

Object Classification in Visual Surveillance Using Adaboost

John-Paul Renno, Dimitrios Makris, Graeme A. Jones

CVPR 2007

doi:10.1109/CVPR.2007.383514 /cvpr/2007/renno2007cvpr-object/

Abstract

In this paper, we present a method of object classification within the context of visual surveillance. Our goal is the classification of tracked objects into one of the two classes: people and cars. Using training data comprised of trajectories tracked from our car-park, a weighted ensemble of Adaboost classifiers is developed. Each ensemble is representative of a particular feature, evaluated and normalised by its significance. Classification is performed using the sub-optimal hyper-plane derived by selection of the N-best performing feature ensembles. The resulting performance is compared to a similar Adaboost classifier, trained using a single ensemble over all dimensions.

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Cite

Text

Renno et al. "Object Classification in Visual Surveillance Using Adaboost." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383514

Markdown

[Renno et al. "Object Classification in Visual Surveillance Using Adaboost." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/renno2007cvpr-object/) doi:10.1109/CVPR.2007.383514

BibTeX

@inproceedings{renno2007cvpr-object,
  title     = {{Object Classification in Visual Surveillance Using Adaboost}},
  author    = {Renno, John-Paul and Makris, Dimitrios and Jones, Graeme A.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2007},
  doi       = {10.1109/CVPR.2007.383514},
  url       = {https://mlanthology.org/cvpr/2007/renno2007cvpr-object/}
}