A framework for robust object multi-detection with a vote aggregation and a cascade filtering
Abstract
This paper presents a framework designed for the multi-object detection purposes and adjusted for the application of product search on the market shelves. The framework uses a single feedback loop and a pattern resizing mechanism to demonstrate the top effectiveness of the state-of-the-art local features. A high detection rate with a low false detection chance can be achieved with use of only one pattern per object and no manual parameters adjustments. The method incorporates well known local features and a basic matching process to create a reliable voting space. Further steps comprise of metric transformations, graphical vote space representation, two-phase vote aggregation process and a cascade of verifying filters.
Cite
@article{arxiv.1512.08648,
title = {A framework for robust object multi-detection with a vote aggregation and a cascade filtering},
author = {Grzegorz Kurzejamski and Jacek Zawistowski and Grzegorz Sarwas},
journal= {arXiv preprint arXiv:1512.08648},
year = {2015}
}
Comments
23rd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG) 2015 Short Paper. Computer Science Research Notes CSRN 2502, ISSN 2464-4617, ISBN 978-80-86943-66-4, 2015