Video Object Recognition and Modeling by SIFT Matching Optimization

Alessandro Bruno, Luca Greco, Marco La Cascia

Abstract

In this paper we present a novel technique for object modeling and object recognition in video. Given a set of videos containing 360 degrees views of objects we compute a model for each object, then we analyze short videos to determine if the object depicted in the video is one of the modeled objects. The object model is built from a video spanning a 360 degree view of the object taken against a uniform background. In order to create the object model, the proposed techniques selects a few representative frames from each video and local features of such frames. The object recognition is performed selecting a few frames from the query video, extracting local features from each frame and looking for matches in all the representative frames constituting the models of all the objects. If the number of matches exceed a fixed threshold the corresponding object is considered the recognized objects .To evaluate our approach we acquired a dataset of 25 videos representing 25 different objects and used these videos to build the objects model. Then we took 25 test videos containing only one of the known objects and 5 videos containing only unknown objects. Experiments showed that, despite a significant compression in the model, recognition results are satisfactory.

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Paper Citation


in Harvard Style

Bruno A., Greco L. and La Cascia M. (2014). Video Object Recognition and Modeling by SIFT Matching Optimization . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 662-670. DOI: 10.5220/0004828006620670


in Bibtex Style

@conference{icpram14,
author={Alessandro Bruno and Luca Greco and Marco La Cascia},
title={Video Object Recognition and Modeling by SIFT Matching Optimization},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={662-670},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004828006620670},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Video Object Recognition and Modeling by SIFT Matching Optimization
SN - 978-989-758-018-5
AU - Bruno A.
AU - Greco L.
AU - La Cascia M.
PY - 2014
SP - 662
EP - 670
DO - 10.5220/0004828006620670