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.

Video object recognition and modeling by SIFT matching optimization, 2014.

Video object recognition and modeling by SIFT matching optimization

Bruno, A.
;
2014-01-01

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.
Inglese
2014
https://www.scitepress.org/Papers/2014/48280/
3rd International Conference on Pattern Recognition Applications and Methods (ICPRAM)
internazionale
contributo
Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
662
670
978-989-758-018-5
Portugal
SciTePress
esperti anonimi
Online
Settore INF/01 - Informatica
3
File in questo prodotto:
File Dimensione Formato  
Paper (1).pdf

Accessibile solo dagli utenti con account Apeiron

Tipologia: Documento in Post-print
Dimensione 948.7 kB
Formato Adobe PDF
948.7 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10808/50446
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 11
  • ???jsp.display-item.citation.isi??? ND
social impact