Energy based descriptors and their application for car detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86093009" target="_blank" >RIV/61989100:27240/14:86093009 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=HcTRp9Ut%2f8M%3d&t=1" target="_blank" >http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=HcTRp9Ut%2f8M%3d&t=1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0004685804920499" target="_blank" >10.5220/0004685804920499</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Energy based descriptors and their application for car detection

Popis výsledku v původním jazyce

In this paper, we propose a novel technique for object description. The proposed method is based on investigation of energy distribution (in the image) that describes the properties of objects. The energy distribution is encoded into a vector of features and the vector is then used as an input for the SVM classifier. Generally, the technique can be used for detecting arbitrary objects. In this paper, however, we demonstrate the robustness of the proposed descriptors for solving the problem of car detection. Compared with the state-of-the-art descriptors (e.g. HOG, Haar-like features), the proposed approach achieved better results, especially from the viewpoint of dimensionality of the feature vector; the proposed approach is able to successfully describe the objects of interest with a relatively small set of numbers without the use of methods for the reduction of feature vector

Název v anglickém jazyce

Energy based descriptors and their application for car detection

Popis výsledku anglicky

In this paper, we propose a novel technique for object description. The proposed method is based on investigation of energy distribution (in the image) that describes the properties of objects. The energy distribution is encoded into a vector of features and the vector is then used as an input for the SVM classifier. Generally, the technique can be used for detecting arbitrary objects. In this paper, however, we demonstrate the robustness of the proposed descriptors for solving the problem of car detection. Compared with the state-of-the-art descriptors (e.g. HOG, Haar-like features), the proposed approach achieved better results, especially from the viewpoint of dimensionality of the feature vector; the proposed approach is able to successfully describe the objects of interest with a relatively small set of numbers without the use of methods for the reduction of feature vector

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications

ISBN

978-989-758-003-1

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

492-499

Název nakladatele

SciTePress - Science and Technology Publications

Místo vydání

[Portugalsko]

Místo konání akce

Lisabon

Datum konání akce

5. 1. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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