Document - Efficient foreground-background segmentation using local features for object detection

2015

Conference article Open Access

Efficient foreground-background segmentation using local features for object detection

Carrara F., Amato G., Falchi F., Gennaro C.

Object Detection Foreground-background segmentation Image processing and computer vision Foregroud-Background segmentation Local features Local features.

In this work, a local feature based background modelling for background-foreground feature segmentation is presented. In local feature based computer vision applications, a local feature based model presents advantages with respect to classical pixel-based ones in terms of informativeness, robustness and segmentation performances. The method discussed in this paper is a block-wise background modelling where we propose to store the positions of only most frequent local feature configurations for each block. Incoming local features are classified as background or foreground depending on their position with respect to stored configurations. The resulting classification is refined applying a block-level analysis. Experiments on public dataset were conducted to compare the presented method to classical pixel-based background modelling.

Source: 9th International Conference on Distributed Smart Cameras, pp. 175–180, Seville, Spain, 8-11/09/2015

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BibTeX entry

@inproceedings{oai:it.cnr:prodotti:346053,
	title = {Efficient foreground-background segmentation using local features for object detection},
	author = {Carrara F. and Amato G. and Falchi F. and Gennaro C.},
	doi = {10.1145/2789116.2789136},
	booktitle = {9th International Conference on Distributed Smart Cameras, pp. 175–180, Seville, Spain, 8-11/09/2015},
	year = {2015}
}