Energy-transfer features for pedestrian detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86089024" target="_blank" >RIV/61989100:27240/13:86089024 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007%2F978-3-642-41939-3_41" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-642-41939-3_41</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-41939-3_41" target="_blank" >10.1007/978-3-642-41939-3_41</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Energy-transfer features for pedestrian detection

Popis výsledku v původním jazyce

In this paper, we propose an interesting and novel method for computing the image features that are useful for object detection. The method is interesting and novel in the terms of the feature vector dimensionality and object information capturing. In the proposed method, the areas of objects (that contain the important information useful for recognition) are described by the distribution of energy. The energy is transfered through the energy sources that are placed into the image and the distribution of energy is encoded into a vector of features. The vector is then used as an input for the SVM classifier. Using this approach, the objects of interest can be successfully described with a relatively small set of numbers if compared with the state-of-the-art descriptors that are based on the histograms of oriented gradients. We show the robustness of the features in the task of pedestrian detection.

Název v anglickém jazyce

Energy-transfer features for pedestrian detection

Popis výsledku anglicky

In this paper, we propose an interesting and novel method for computing the image features that are useful for object detection. The method is interesting and novel in the terms of the feature vector dimensionality and object information capturing. In the proposed method, the areas of objects (that contain the important information useful for recognition) are described by the distribution of energy. The energy is transfered through the energy sources that are placed into the image and the distribution of energy is encoded into a vector of features. The vector is then used as an input for the SVM classifier. Using this approach, the objects of interest can be successfully described with a relatively small set of numbers if compared with the state-of-the-art descriptors that are based on the histograms of oriented gradients. We show the robustness of the features in the task of pedestrian detection.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science. Volume 8034

ISBN

978-3-642-41938-6

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

10

Strana od-do

425-434

Název nakladatele

Springer Verlag

Místo vydání

London

Místo konání akce

Rethymnon

Datum konání akce

29. 7. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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